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This study explored using big data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents' Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell (n = 13) and those who did not fall (n = 10). We analyzed associations between participants' fall events (n = 69) and pre-fall changes in in-home gait speed and stride length (n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall (p < .0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed. Results demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.
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Using Embedded Sensors in Independent Living to Predict Gait
Changes and Falls
Lorraine J. Phillips1, Chelsea B. DeRoche1, Marilyn Rantz1, Gregory L. Alexander1, Marjorie
Skubic1, Laurel Despins1, Carmen Abbott1, Bradford H. Harris1, Colleen Galambos1, and
Richelle J. Koopman1
1University of Missouri, Columbia, USA
This study explored using big data, totaling 66 terabytes over 10 years, captured from sensor
systems installed in independent living apartments to predict falls from pre-fall changes in
residents’ Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait
parameters continuously collected for residents who actually fell (
= 13) and those who did not
fall (
= 10). We analyzed associations between participants’ fall events (
= 69) and pre-fall
changes in in-home gait speed and stride length (
= 2,070). Preliminary results indicate that a
cumulative change in speed over time is associated with the probability of a fall (
< .0001). The
odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the
odds of a resident falling within 3 weeks after no change in in-home gait speed. Results
demonstrate using sensors to measure in-home gait parameters associated with the occurrence of
future falls.
sensors; falls; gait speed; stride length; older adults
The ability of nurses to detect changes in health conditions, functional decline, or increasing
fall risk in aging residents who live in the community is a critical part of patient assessment.
Historically, these types of assessments have included direct observation, surveys, and
interviews to evaluate a resident’s condition change or declining functional status
(Rubenstein et al., 2004; Tideiksarr, 2003; Tinetti, 2003). Although many of these
assessments used by nurses have been validated and are reliable sources of data for trending
status changes in community-dwelling residents, they require a health care provider to be
present for the data collection and evaluation. Cutting edge models of care using sensor
technology that collects data 24/7 and results in many terabytes of data over several years
are being incorporated into residents’ living quarters in elder living communities to facilitate
faster decisions about nursing care needed and to make selected living environments safe for
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Corresponding Author: Lorraine J. Phillips, Sinclair School of Nursing, University of Missouri, S414 Nursing, Columbia, MO
65211, USA.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
HHS Public Access
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older people (Dawadi, Cook, & Schmitter-Edgecombe, 2014; Reeder et al., 2013; Skubic,
Alexander, Popescu, Rantz, & Keller, 2009). However, because of their age, frail state, and
often times declining health, these residents may require enhanced nursing care
coordination. Sensor technology is being used to detect changes in a resident’s condition or
an episode of functional decline sooner, enhancing nurses’ ability to react to episodic
condition changes, perform a resident assessment more quickly, and possibly suggest
treatment options sooner (Rantz et al., 2013). We describe prediction of older adult fall risk
using big data sets captured over many years by multiple sensing devices and nurse care
coordinators’ use of these data in the care of these adults.
Fall Detection and Activity Monitoring Using Sensor Technology
Falls are the primary cause of injury in older adults, occurring at an annual rate of 33% in
persons above the age of 65 years (Centers for Disease Control and Prevention [CDC],
2015a). Early and efficient identification of older adults at risk for falls could prevent costly
injuries and loss of quality of life. The extant literature shows that people who walk slower
with shorter strides are more likely to fall (Barak, Wagenaar, & Holt, 2006; Quach et al.,
2011), but older adults do not receive regular gait evaluations. In an effort to increase fall
risk screening efforts, the CDC developed the Stopping Elderly Accidents, Deaths, and
Injuries (STEADI) toolkit for health care providers to use in clinical practice (CDC, 2015b).
Toolkit materials instruct providers how to conduct specific tests known to predict fall risk,
such as the Timed Up and Go, 30-s chair stand, and four-stage balance tests (Guralnik et al.,
1994; Jones, Rikli, & Beam, 1999; Podsiadlo & Richardson, 1991). However, providers face
time limitations, competing demands, and reimbursement constraints in evaluating and
managing fall risk among their patients (Tinetti, Gordon, Sogolow, Lapin, & Bradley, 2006).
Moreover, most falls go unreported, further limiting opportunities for providers to assess risk
and recommend preventive measures (Shumway-Cook et al., 2009).
One solution to the problem of underreported falls and existing gaps in fall risk screening
among older adults is in-home monitoring systems that passively and continuously capture
gait parameters from which fall risk can be predicted (Barak et al., 2006). Until recently,
human motion analysis using video required the attachment of body markers, as with the
Vicon system or the use of multiple cameras, and intensive computations to extract enough
silhouettes to fit a skeletal model (Stone & Skubic, 2011). In 2011, the Center for Eldercare
and Rehabilitation Technology research team at the University of Missouri (MU) developed
a methodology to obtain measurements of temporal and spatial gait parameters from the
Microsoft Kinect sensor. Using both a Vicon motion capture system and a web-camera-
based system to provide ground truth, researchers validated Kinect-recorded three-
dimensional (3D) depth images against standardized gait and balance measures. In addition,
fall-detection algorithms were initially developed with stunt actors performing falls in the
laboratory and further validated using stunt actors in the apartments of residents of
TigerPlace, an independent living community and MU research partner. Following iterative
refinement of the fall-detection algorithms, the Kinect fall-detection system has been
installed in a total of 39 TigerPlace apartments for continuous automated monitoring of gait
parameters for fall risk as well as automated fall detection in the apartments.
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As interest in smart home technologies burgeons, other applications of Kinect sensors are
being tested (Reeder et al., 2013). Ejupi and colleagues (2016) used the Kinect in a
prospective study of fall events in community-dwelling older adults. They demonstrated that
upper extremity reaction time was significantly slower for older adults who experienced a
fall in the subsequent 6 months compared with those with no fall events. Ejupi et al.
envisioned the system could eventually be installed in clinical and home settings for ongoing
monitoring of fall risk. In other research, a Kinect sensor was paired with inertial sensors
attached to eating utensils for the purpose of tracking movements of interest to therapists
(Hondori, Khademi, & Lopes, 2012). In addition, rehabilitation exercise games using the
Kinect sensor are under development for use in the home setting (Shapi’i, Bahari, Arshad,
Zin, & Mahayuddin, 2015). Although this research demonstrates a variety of clinical uses
for the Kinect sensor, our research has advanced smart home technology by real-world
operation of the Kinect sensor in older adults’ residences.
Currently, novel sensor systems at TigerPlace use multimodal data sources to collect an
array of data 24/7 about residents. Some types of sensor systems are listed and described in
Table 1. These data sources extend the ability of TigerPlace’s nurses to detect baseline
changes in a resident’s activity levels, without having to be physically present for the
assessment. This is a shift from traditional models of assessment. For example, wall-
mounted sensor systems help nurses detect when a resident is not as active in their whole
apartment, and are spending more time in bed than usual.
Figure 1 illustrates motion density sensor data visualized through an electronic interface
used by nurses to evaluate activity in resident apartments in independent living. This specific
illustration also shows that an alert was generated based on a computation using a pre-
determined number of standard deviations change from the resident’s normal activity level.
An alert email was also sent to a nurse indicating that the resident was having increased
motion in their apartment on September 30, 2015. This increased motion can be seen by the
light gray highlighted areas between 2:00 and 6:00 a.m. on September 30, 2015. In addition,
the resident appears to be leaving the apartment (black areas between 2:00 and 6:00 a.m. on
September 30, 2015) during this same time frame. Based on the patterns observed in this
interface, this activity is irregular activity for this person compared with other nights in this
density map. This irregular activity is a situation that a nurse may want to review. Case study
analyses indicate that increasing periods of inactivity (decreased motion density) could
indicate the resident is experiencing higher levels of depression (Galambos, Skubic, Wang,
& Rantz, 2013), or it could indicate that the resident is experiencing an illness that is causing
greater fatigue and weakness, prompting more rest. Inactivity (black areas) could also mean
that the resident has left the apartment at unusual periods of the night or day which may be
an indicator of confusion or altered cognitive state.
The sensor system used in this research provides a complex and challenging opportunity to
combine different modes of sensor data input that can be triangulated to help nurses detect
changes in health conditions and functional decline in frail elders. Not only do sensor system
modalities help build a better picture of a resident’s current health status, but with the
addition of the Kinect sensors, they provide automated assessment of in-home gait
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parameters and actual falls. Obtaining gait pattern data provides supplemental information
about residents’ health status because research has shown that—down to the 10th of a meter
per second—an older person’s pace, along with their age and gender, can predict their life
expectancy just as well as the complex battery of other health indicators (Harmon, 2011).
Therefore, to investigate whether Kinect sensor data could be used to identify individuals at
increased risk of falling, we analyzed the association between pre-fall changes in Kinect-
recorded gait parameters and known fall events in TigerPlace residents with Kinect sensors.
TigerPlace is a senior housing community in Columbia, Missouri, with 54 independent
living apartments. Named for the MU mascot, it is an innovative living environment built
and operated by Americare Senior Living, in affiliation with the MU Sinclair School of
Nursing. Infrastructure is in place to support sensor networks in TigerPlace apartments.
TigerPlace provides a unique opportunity in which to develop and evaluate technology in a
collaborative setting with researchers from MU departments of Electrical and Computer
Engineering, Computer Science, Health Management and Informatics, and Schools of
Nursing, Health Professions, Social Work, and Medicine. These researchers form an
interdisciplinary team to develop and implement projects that improve the quality of life and
care of seniors. No other setting in the United States offers the population of subjects,
research infrastructure, faculty, and resources like TigerPlace; research projects are
encouraged, and residents who choose to participate enjoy the experience of developing new
technologies to help seniors’ age in place. TigerPlace residents, like other seniors in
Missouri and the United States, are concerned about maintaining independence and dignity.
TigerPlace is a key component of the Sinclair School of Nursing Aging in Place project that
was designed with MU faculty working with Americare Systems, Inc., Sikeston, Missouri,
to promote the independence of older adults. Nurses, physical therapists, occupational
therapists, and architects with an emphasis in environmental design participated in the
building plan. At TigerPlace, residents can age in place without fear of being moved to a
traditional nursing home unless they choose to leave. Findings indicate that with the right
supportive and restorative services, it is possible to help elders improve their health and
well-being and delay or prevent nursing home placement (Rantz et al., 2005). Registered
nurse (RN) care coordinators direct the wellness center that offers exercise and other health-
related classes, and conduct regular health assessments to promote residents’ well-being and
vitality. An electronic health record is used to document day-to-day care. Aging in Place
staff provide an array of home care services such as medication management, assistance
with activities of daily living, care coordination of health conditions, communication with
residents’ physicians and other health care providers, psychosocial services, and Medicare
home health care when residents need and qualify for that service.
Data required for the analysis in the present study were available for a convenience sample
of 23 TigerPlace participants with Kinect sensor systems and were collected over intervals
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ranging from 3 to 48 months, the length of which depended on the duration of participants’
enrollment in the study and length of stay in the community. We excluded participants living
with another person because when co-habiting individuals have similar height and gait
velocities, their unique clusters of gait data are not discernable in the collective data. All
participants were Caucasian and 70% were female. At the time of admission to TigerPlace,
participants were, on average, 85.2 years of age, and at the time of data analysis, had a mean
length of stay of 49 months. As with TigerPlace research, the institutional review board at
MU approved the research and participants provided written informed consent.
Sensor Data Collected
The Kinect sensor continuously monitors and records residents’ in-home movement
occurring within sensor range. The Kinect sensor is typically located on a small shelf above
the front door to maximize the camera’s view of activity in the main living area. A computer
that logs and transmits the Kinect depth image data is located in a nearby kitchen cabinet.
Data are processed as depth images that appear as silhouettes to protect privacy. If the Kinect
system detected a potential fall, an alert was sent to staff who responded to check on the
resident. If the resident was found on the floor in response to a fall alert or the resident self-
reported a fall in response to the alert, the fall was recorded in the resident’s health record.
In the present study, a fall was defined as an observed or reported unexpected event in which
the resident came to rest on the ground or lower surface. In addition, falls that did not
generate an alert but that TigerPlace staff observed or falls that were otherwise reported, for
example, by residents themselves or family members, were recorded in residents’ health
records. After extracting fall event data from health records, we were able to determine the
frequency of falls for TigerPlace residents with Kinect system installations and then analyze
the association between participants’ fall events and change in in-home gait speed and stride
length. Figure 2 provides an illustration of the interface developed to display gait parameter
sensor data, longitudinally for 30 days. Figure 3 represents fall events for a participant
plotted along weekly gait speed estimates during an 87-week period.
Data Collection
Skeletal tracking with the Kinect Software Development Kit is limited to a range of 1.5 to 4
m, which is insufficient to capture walking sequences in some areas of TigerPlace
apartments. Therefore, raw disparity values from the Kinect depth stream, which is a
subtraction technique to separate foreground 3D objects (e.g., the person walking) from
background stationary objects, such as furniture, are processed to yield walk data for
distances of up to 8 m from the Kinect (Stone & Skubic, 2013). Algorithms developed for
the Kinect sensor automatically identify, segment and analyze walking segments of at least
1.2 m occurring within sensor range for gait speed, stride time, stride length, and height of
the individual walking. The height of each resident was measured a priori so that a cluster, or
mode, in the dataset is recognized as belonging to that resident. Modes that closely match
the known height of a resident are used for resident model initialization. Typically, a range
of 4 to 35 walks per day are required to identify walks belonging to a resident and not
another person. Assuming a dataset of walks exists for a resident, the model is updated using
data from a given time interval, or model window size, which may be as short as 2 weeks or
as long as 3 months. A longer model window size may be required to initialize a model for a
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resident who walks infrequently. Stride parameters are extracted only for walks for which at
least five sequential steps can be identified. Further information on data science methods can
be accessed in related papers at the following website:
A recent subset of the data (e.g., 1–2 weeks) used in model initialization is used to calculate
gait parameters. A 2-week gait parameter window size reflects longer term changes
compared with short-term fluctuations that may be represented in a 1-week gait parameter
window. Gait parameter estimates for any given day are calculated as a weighted average of
gait data from all walks identified in that resident’s apartment within the gait parameter
window. A weight is applied to each walk based on the likelihood that it reflects the resident
model. The window for both the model estimation and gait parameter estimation slide
forward one day and the process of model estimation repeats itself using the previous model
estimate for the update procedure. The process of identifying walking segments, extracting
gait parameters, updating resident models, and calculating gait parameters was and
continues to be completely automated. Data recorded by the sensor systems are continuously
logged 24/7 by participant identification number and stored on a secure server, of which
Kinect data account for 66 terabytes being used to develop algorithms for gait speed change
and fall detection. In totality, sensor network data account for 82 terabytes of storage space.
Depending on the length of time over which gait data are collected, daily gait parameter data
may be available in model window sizes of 30, 60, and 90 days and gait parameter window
sizes of 7 and 14 days, yielding up to six possible estimates each day for each gait parameter
(i.e., gait speed, stride length, stride time). For this study, window sizes of 30 days and 14
days were used for the model and gait parameter estimation steps, respectively. These
window sizes were selected so that a sufficient number of walks were included to allow
accurate resident modeling but not excluding cases lacking data that would be needed to
compute model window sizes of 60 or 90 days.
Data Analysis
Daily gait parameter estimates were available for 23 participants. For those residents that
had falls during the study period, we calculated cumulative change in gait parameters from
30 days before a fall. For those residents that had no falls during the study period, we chose
a random 30-day window to serve as a control. For a fall to be included in the model, the
resident had to have at least 14 days of gait parameter estimates (not necessarily
consecutive) or no more than 16 days missing for the entire 30-day window before a fall
event. A total of 69 falls met the aforementioned criteria, which resulted in 2,070
observations of gait parameters. We estimated a logistic regression model to predict the odds
and the probability of a fall event based on cumulative change in gait parameters at intervals
of 7, 14, 21, and 28 days. The outcome was whether a resident had a fall at the end of the
30-day window, and the predictor variable was cumulative change stratified by time
(numerical day in the 30-day window). Two separate models were run for cumulative change
in in-home gait speed and cumulative change in in-home stride length stratified by time. All
analyses were run in SAS 9.4 with a significance level of .05.
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Ten of the 23 participants had no falls during the monitoring period, which varied by length
of residence from 2011 to 2015. Participants with no fall events were, on average, 85 years
of age on admission to TigerPlace and had resided there for an average of 44 months at the
time of data collection. Residents who had fallen (
= 13) had between 1 and 12 fall events,
were 85.4 years of age, and had resided at TigerPlace for an average of 52 months.
Preliminary results indicate that cumulative change in speed over time is significantly
associated with probability of a fall (
< .0001). The odds of a resident falling within 3
weeks after a cumulative decline of 2.54 cm/s over 7 days is 4.22 (95% confidence interval
[CI] = [2.14, 8.30]) times the odds of a resident falling within 3 weeks after no change in in-
home gait speed. The model estimates that a cumulative decrease of 5.1 cm/s over 7 days in
in-home gait speed is associated with an 86.3% probability of falling within the next 3
weeks compared with a 19.5% probability for those with no change. The area under the
curve (ROC) is 0.86 indicating that cumulative change in gait speed is good at separating
residents that have fallen from non-fallers.
Similarly, preliminary results show that cumulative changes in stride length over time are
significantly associated with probability of a fall (
value < .0001). The odds of a resident
falling within 3 weeks after a cumulative change of 2.54 cm over 7 days is 6.78 (95% CI =
[2.69, 17.07]) times the odds of a resident falling within 3 weeks after no change in in-home
stride length. This model estimates that a cumulative decrease of 7.6 cm over 7 days in in-
home stride length is associated with a 50.6% probability of falling within the next 3 weeks
compared with 11.4% probability for those with no change. The ROC is 0.88 which
indicates that cumulative change in stride length is good at separating residents who have
fallen from non-fallers.
Our results demonstrate the feasibility of using environmentally embedded sensors to
measure in-home gait parameters associated with the occurrence of falls. The Kinect sensor
system holds promise for unobtrusively monitoring older adults in their homes while
maintaining privacy and eliminating the burden of additional monitoring procedures. The
Kinect sensor system does not require the older adult to perform gait tests, wear devices, or
push a button after a fall. In addition, in-home gait parameters can be measured continuously
so that changes over time can be automatically and promptly detected, alerts generated, and
appropriate medical or therapy referrals arranged. In response to deterioration in gait speed
or stride length, best practice guidelines for screening and assessment and fall prevention
interventions could be initiated before a fall occurs and significant injury or disability ensues
(Kenny et al., 2011). Residents report feeling more secure in their home environment
knowing that the sensors will detect falls and that assistance will be provided when needed
(Jacelon & Hanson, 2013; Rantz et al., 2015).
Our model estimated that a 5.1 cm/s (i.e., 0.051 m/s) decrease in in-home gait speed was
associated with a high probability of falling within 3 weeks, and this is consistent with
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published thresholds of 0.05 m/s for a small meaningful change in gait speed assessed over
distances of 10 foot, 10 m, or 4 m (Perera, Mody, Woodman, & Studenski, 2006). Similarly,
Kwon and colleagues’ (2009) analysis of gait speed change for 424 older adults in a clinical
exercise trial showed that a change of 0.03 to 0.05 m/s corresponded to an effect size of 0.2
and a change of 0.08 m/s corresponded to an effect size of 0.5. More importantly, that we
can detect similar declines in gait speed by recording and analyzing walking segments as
older adults go about their everyday activity means that decline may be detected earlier and
clinical interventions initiated sooner than would occur by intermittent clinical evaluation.
Benefits of early interventions in the presence of declining walk parameters extend beyond
reducing fall risk. Improvement, as opposed to decline, in gait speed of 0.1 m/s over 1 year,
has been associated with a 58% reduction in relative risk of mortality over the subsequent 8
years (Hardy, Perera, Roumani, Chandler, & Studenski, 2007).
Another approach to unobtrusive and ongoing gait assessment involves using passive
infrared motion sensors (Kaye et al., 2012). Kaye and colleagues (2012) used sensors
positioned in a sensor line (e.g., attached to the ceiling of a hallway or corridor) in the homes
of 76 community-dwelling older adults to estimate gait speed from the pattern and time
intervals of sensor firings and validated these estimates against performance-based mobility
tests. Furthering this work, Rana, Austin, Jacobs, Karunanithi, and Kaye (2013)
demonstrated that transition time between rooms, measured with infrared sensors positioned
throughout the dwelling, not only correlated highly with gait velocity measured along the
“sensor line” (
2 = .98) but yielded a greater number and variety of gait velocity estimates.
Although infrared sensors offer a compelling alternative to camera-based sensors for
measuring in-home gait speed, they lack the fall-detection functionality that our team has
developed with Kinect sensors.
The relevance of continuous in-home gait speed analysis is to be able to apply these findings
to the ongoing, real-time data and to automatically send alerts about increasing risk of a fall
to the older adult, nearby family members, and/or health care staff. With 3 weeks of lead
time, the older person could seek assistance to improve their functional capacity or health
status and possibly avoid the fall. Having lead time to alert the older person and others has
enormous potential to change the loss of functional abilities, trauma, and potentially death
that result from falls.
In addition, by identifying the in-home gait speed change threshold at which fall risk is
substantially increased, alert algorithms can be further refined for greater specificity. Rather
than use the pre-determined standard deviation multiplier developed initially through
retrospective analysis and clinician expertise, new alert parameters based on absolute change
in gait speed and stride length can be tested alongside the current alert algorithm. Refining
alerts for greater specificity is important not only to promptly identify decline in elders’
physical function but also to prevent false alarms that cause clinicians to become alert-
fatigued and desensitized to safety alerts (Agency for Healthcare Research and Quality,
2015). Our team is committed to ongoing efforts that advance sensor technology research
and optimize clinical relevance to support aging in place for the older adult population.
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Our analysis is not without several limitations. One set of limitations is related to
generalizability of the findings. Although our sample of fall events included 69 falls, these
fall events came from a participant sample of 23 individuals and were restricted to
TigerPlace residents. Because TigerPlace residents receive RN care coordination services
and may age in place as their health and function decline, it is unclear how representative the
sample is of other independent living community residents.
Second, as noted above, not all falls were directly observed or captured by the Kinect sensor.
The accuracy of fall history could not be confirmed in some cases. That being said, many
studies on fall risk factors and fall incidence rely on self-reports of fall events as most falls
go unwitnessed (Barak et al., 2006; Rosen, Mack, & Noonan, 2013; Shumway-Cook et al.,
2009). However, “falls” were not explicitly defined within the health record at TigerPlace
and thus were subject to clinicians’ interpretation of post-fall circumstances or residents’
Third, our analysis was a retrospective analysis of previously recorded sensor data. Use of
existing data not only has the advantages of reduced cost and burden but also, in the present
study, has the disadvantages of missing data related to sensor system failure and missing
data related to inadequate number of walks for resident modeling. It is not possible to know
whether residents with too few walks to compute individual models had fall events that were
not included in the analysis. Finally, apartments with greater than one resident were
excluded from the analysis because resident models could not be computed accurately at the
time the data were analyzed.
Future research could involve intervention trials to determine whether fall rates differ for
senior housing residents who receive therapeutic interventions based on real-time gait
parameter data from Kinect sensors compared with senior housing residents who receive
standard of care. Assisted living would be an ideal setting in which to test the Kinect sensor-
alert system because 24-hr staff are available to respond to fall alerts and falls are a major
problem in this setting, affecting 21% of residents in any 90-day period (Sengupta, Harris-
Kojetin, & Caffrey, 2015). In addition, future research to refine the alert algorithms as
described above could occur in tandem with a clinical trial or large-scale deployment to
yield greater accuracy in algorithmic predictions.
Sensor systems that assist nurse care coordinators to detect functional decline including
changes in gait that indicate an increased fall risk and actual falls are sorely needed to
facilitate earlier detection, treatment, and prevention of these costly events. Traditional
systems of assessment that require nurses to complete unidimensional instruments are risky,
because they are not as timely and they typically do not incorporate multiple assessment
modalities in a living environment like the sensors described in this study. Another benefit of
sensor data collected over months and years on the same residents is the longitudinal nature
of this big data that enhances the nurse’s ability to detect changes in baseline over time
much more effectively than do traditional assessment methods.
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The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of
this article: This work was supported by the National Institutes of Health (R01NR014255).
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Figure 1.
Motion sensor data density visualization including night-time motion density: Increase alert.
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Figure 2.
Gait parameter interface showing calculated stride length and time, gait speed, and alerts.
TUG = Timed Up and Go.
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Figure 3.
Weekly gait speed averages with falls plotted on in-home gait speed graph.
= average gait speed; = fall event.
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Table 1
Sensor System Modalities and Descriptions.
Type of sensor Description
Passive infrared
(PIR) Installed to detect presence in a particular room (e.g., living
room, by the door or bathroom) as well as for specific
activities. For example, a motion sensor installed on the
ceiling above the shower detects showering activity.
Bed sensor The bed sensor is a set of four hydraulic transducers installed
under the mattress which captures a ballistocardiogram,
respiration, and bed restlessness as someone lies on the bed.
Specialized signal processing is used to compute heart rate,
respiration rate, and restlessness every 15 s.
Depth camera A depth camera is used to compute gait parameters (gait
speed, walks/day, and stride length). The depth camera also
uses a fall-detection algorithm to detect falls in real-time and
send alerts to designated individuals (e.g., clinical staff) with a
link to a depth video (shadow-like silhouette images) showing
the fall.
Doppler radar Doppler radar systems have been tested to measure gait
parameters and to detect falls. A Doppler radar transmits an
electromagnetic wave at a specific frequency and measures
the shifts in the reflected waves, like weather radars. These
frequency shifts can then be used to obtain the velocities of
the person’s body parts in the radar’s direction.
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... Our team has used depth sensors in elderly populations in TigerPlace, an aging-inplace facility, and assisted living facilities with good success for fall detection and gait monitoring, starting in 2011 [58,59]. Building on this work, we developed a Kinect-based system for daily activity recognition and assessment (DARAS). ...
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Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients’ health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily action logger was implemented with a Foresite Healthcare depth sensor. Daily activity data were collected from seventeen post-stroke participants’ homes over three months. Given the extensive amount of data, only a portion of the participants’ data was used for this specific analysis. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network, which learns rich spatial-temporal features from both depth and skeletal joint data, fuses the prediction outputs from a customized 3D convolutional–de-convolutional network, customized region convolutional 3D network, and a proposed region hierarchical co-occurrence network. The per-frame precision and per-action precision were 0.819 and 0.838, respectively, on the test set. The outcomes from the DARAS can help clinicians to provide more personalized rehabilitation plans that benefit patients.
... This enables caregivers to prioritize attention for individuals who are at a higher risk of falling. An example of this is presented in [193], where the authors analyze gait parameters to predict the likelihood of falls occurring within the next three weeks. ...
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In the elderly population, falls are one of the leading causes of fatal and non-fatal injuries. Fall detection and early alarms play an important role in mitigating the negative effects of falls, especially given the growing proportion of the elderly population. Due to their non-intrusive nature, data availability, and low deployment costs, RGB videos have been used in many previous studies to detect falls. The RGB data, however, can be affected by background environment changes, resulting in non-recognition. To overcome these challenges, many researchers propose extracting skeleton data from RGB videos and using it for fall detection. Although there have been multiple surveys on fall detection, most of them focus on assessing fall detection systems using different kinds of sensors, and a comprehensive evaluation of skeleton-based fall detection in RGB videos is lacking. In this paper, we examine the most recent advances in skeleton-based fall detection in RGB videos, from handcrafted feature-based methods to advanced deep learning algorithms. Further, we present several skeleton-based fall detection techniques and their performance results on various benchmark datasets, along with challenges and future directions in this field.
... Additionally, change in gait speed could be used to identify older adults whose gait speed is below the commonly applied fall risk threshold (i.e., 1.0 m/s), but who are experiencing a further decline in gait speed. Change in gait speed and fall risk has been investigated previously, but those studies were limited by small sample sizes [17,18], short study duration, and infrequent measures of gait speed [14,19]. While gait speed typically declines with advancing age [20], increases in gait speed do occur in older adults, for example following participation in balance and exercise programs [21][22][23][24], which is in turn associated with decreased fall risk [24]. ...
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Background Although slow gait speed is an established risk factor for falls, few studies have evaluated change in gait speed as a predictor of falls or considered variability in effects by cognitive status. Change in gait speed may be a more useful metric because of its potential to identify decline in function. In addition, older adults with mild cognitive impairment are at an elevated risk of falls. The purpose of this research was to quantify the association between 12-month change in gait speed and falls in the subsequent 6 months among older adults with and without mild cognitive impairment. Methods Falls were self-reported every six months, and gait speed was ascertained annually among 2,776 participants in the Ginkgo Evaluation of Memory Study (2000–2008). Adjusted Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for fall risk relative to a 12-month change in gait speed. Results Slowing gait speed over 12 months was associated with increased risk of one or more falls (HR:1.13; 95% CI: 1.02 to 1.25) and multiple falls (HR:1.44; 95% CI: 1.18 to 1.75). Quickening gait speed was not associated with risk of one or more falls (HR 0.97; 95% CI: 0.87 to 1.08) or multiple falls (HR 1.04; 95% CI: 0.84 to 1.28), relative to those with a less than 0.10 m/s change in gait speed. Associations did not vary by cognitive status (pinteraction = 0.95 all falls, 0.25 multiple falls). Conclusions Decline in gait speed over 12 months is associated with an increased likelihood of falls among community-dwelling older adults, regardless of cognitive status. Routine checks of gait speed at outpatient visits may be warranted as a means to focus fall risk reduction efforts.
... Kinect was deployed in the apartments of the elderly in an independent living facility to analyze gait characteristics by continuous in-home gait measurement [13]. Prediction of falls from pre-fall changes was explored based on the Kinect-recorded gait parameters over 10 years for the residents of independent living apartments [14]. A health monitoring system based on Kinect was developed to categorize movements during walking, standing up, and sitting down as normal or unusual [15]. ...
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The increasing geriatric population across the world has necessitated the early detection of frailty through the analysis of daily-life behavioral patterns. This paper presents a system for ambient, automatic, and the continuous measurement and analysis of ascent and descent motions and long-term handrail-use behaviors of participants in their homes using an RGB-D camera. The system automatically stores information regarding the environment and three-dimensional skeletal coordinates of the participant only when they appear within the camera’s angle of view. Daily stair ascent and descent motions were measured in two houses: one house with two participants in their 20s and two in their 50s, and another with two participants in their 70s. The recorded behaviors were analyzed in terms of the stair ascent/descent speed, handrail grasping points, and frequency determined using the decision tree algorithm. The participants in their 70s exhibited a decreased stair ascent/descent speed compared to other participants; those in their 50s and 70s exhibited increased handrail usage area and frequency. The outcomes of the study indicate the system’s ability to accurately detect a decline in physical function through the continuous measurement of daily stair ascent and descent motions.
... The use of sensor technology in elderly care has shown positive effects in various aspects of well-being, for example, identifying falls [11] and recognizing an elevated risk of falls [12] and noticing deterioration in general health through the monitoring of daily activities [13]. Sensor-generated information about mobility can also be used in the early detection of memory diseases or mental health problems [14]. ...
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Purpose The behavioural activity pattern is a behavioural and biological 24-hour rhythm. Ageing, diseases and memory disorders can change this pattern. Home care staff can utilize knowledge about the behavioural activity pattern of elderly home care clients in many ways. The purpose of this study was to evaluate whether home care staff could identify the behavioural activity pattern of elderly home care clients using activity sensors, namely, actigraphs and motion sensors, could identify the behavioural activity rhythms. Materials and methods A total of four elderly home care clients and one elderly home rehabilitation client took part in the study. The participants wore actigraphs on their wrist and motion sensors were installed in their apartment. In addition to sensor data, home care staff answered one open-ended question during each home care visit. The data collection period was two weeks. Both quantitative and qualitative methods were used in the analysis. Results The behavioural activity pattern was easy to identify from the motion sensor data, whereas actigraph data were difficult to interpret. The home care staff members’ answers to open-ended questions reinforced the reliability of motion sensor data. Conclusions Motion sensors are relatively cheap, unobtrusive and reliable way to identify and detect changes in the behavioural activity patterns of elderly home care clients. • Implications for rehabilitation • Motion sensors are cheap, user-friendly and highly accepted technology for identifying and monitoring behavioural activity rhythm. • Home care staff members can use the data about elderly home care client's behavioural activity rhythm to monitor deviations to the rhythm and detect changes in client's health. • The information about behavioural activity rhythm can also be utilized in planning home care visits and interventions
... Disorders of balance and gait are particularly important in the elderly because they compromise independence and contribute to the risk of falls and injury (27). It has been shown that when walking at a constant speed, the elderly with slower step speed and frequency, shorter stride length and stride length, longer foot support time and greater gait variability are more likely to fall (28)(29)(30). In addition, visual impairment itself . ...
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Objective To examine the risk factors for falls in elderly patients with visual impairment (VI) and assess the predictive performance of these factors. Methods Between January 2019 and March 2021, a total of 251 elderly patients aged 65–92 years with VI were enrolled and then prospectively followed up for 12 months to evaluate outcomes of accidental falls via telephone interviews. Information of demographics and lifestyle, gait and balance deficits, and ophthalmic and systemic conditions were collected during baseline visits. Forward stepwise multivariable logistic regression analysis was performed to identify independent risk factors of falls in elderly patients with VI, and a derived nomogram was constructed. Results A total of 143 falls were reported in 251 elderly patients during follow-up, with an incidence of 56.97%. The risk factors for falls in elderly patients with VI identified by multivariable logistic regression were women [odds ratio (OR), 95% confidence interval (CI): 2.71, 1.40–5.27], smoking (3.57, 1.34–9.48), outdoor activities/3 months (1.31, 1.08–1.59), waking up frequently during the night (2.08, 1.15–3.79), disorders of balance and gait (2.60, 1.29–5.24), glaucoma (3.12, 1.15–8.44), other retinal degenerations (3.31, 1.16–9.43) and best-corrected visual acuity (BCVA) of the better eye (1.79, 1.10–2.91). A nomogram was developed based on the abovementioned multivariate analysis results. The area under receiver operating characteristic curve of the predictive model was 0.779. Conclusions Gender, smoking, outdoor activities, waking up at night, disorders of balance and gait, glaucoma, other retinal degeneration and BCVA of the better eye were independent risk factors for falls in elderly patients with VI. The predictive model and derived nomogram achieved a satisfying prediction of fall risk in these individuals.
... Fall sensors Sensors A good example of combining or modifying existing technology is the use of the Microsoft Kinect Sensors (originally used for gaming) to monitor older adults at home who are at risk for falls. 59 This study showed that a cumulative change in gait speed over time is associated with an increased risk of falling. Because falls in older adults are another by-product and cause of comorbidities, using telehealth and PDH to predict and prevent them is a brilliant idea. ...
The effectiveness of telehealth and personalized digital health became evident during the coronavirus disease 2019 pandemic. This article defines what personalized digital health is and provides selected examples of the various personalized digital health devices patients may be using. The article also delves into how to implement and incorporate these personalized digital health devices in practice and presents suggestions on political actions that nurse practitioners need to advocate for with regard to telehealth and personalized digital health policy.
Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people’s quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people’s daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state of art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR. Keywords: Human activity recognition, wearable sensors, deep learning, features, healthcare
This literature review examines today’s pursuit of healthcare-related technology, embedded into the environment that may assist older adults to remain living independently. Embedded sensors can be sensors embedded into the walls of the home such as cameras, motion detectors, and infrared detectors. Sensors are electronic devices that transmit data captured to a computer that analyzed the data using algorithms and transmit data to caregivers. The caregivers use the data to help them determine if changes in the resident’s health have occurred. Embedded sensors have been used to determine if changes to activity levels have occurred (such as ambulation or exercise), cognitive impairment, driving, mental health, fall risk, actual fall detection in real time, and chronic health problems. Finally, how older adults feel about accepting this technology in their homes and privacy issues are discussed. Embedded sensors can have an impact on the health of older adults and may assist older adults in living independently longer.KeywordsEmbedded sensorsOlder adultsHealth outcomesDepressionMental healthDiabetesActivity pattern recognitionFallsFall riskDrivingChronic health conditions
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Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of passive infrared motion sensors in the consecutively visited room-pair carry rich latent information about a person's gait velocity. We name this time gap transition time and modeling the relationship between transition time and gait velocity, and using a support vector regression approach, we show that gait velocity can be estimated with an average error of <2.5 cm/s. Our method is simple and cost effective and has advantages over competing approaches such as: obtaining 20-100 times more gait velocity measurements per day. It also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time.
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Background Quick protective reactions such as reaching or stepping are important to avoid a fall or minimize injuries. We developed Kinect-based choice reaching and stepping reaction time tests (Kinect-based CRTs) and evaluated their ability to differentiate between older fallers and non-fallers and the feasibility of administering them at home. Methods A total of 94 community-dwelling older people were assessed on the Kinect-based CRTs in the laboratory and were followed-up for falls for 6 months. Additionally, a subgroup (n = 20) conducted the Kinect-based CRTs at home. Signal processing algorithms were developed to extract features for reaction, movement and the total time from the Kinect skeleton data. Results Nineteen participants (20.2 %) reported a fall in the 6 months following the assessment. The reaction time (fallers: 797 ± 136 ms, non-fallers: 714 ± 89 ms), movement time (fallers: 392 ± 50 ms, non-fallers: 358 ± 51 ms) and total time (fallers: 1189 ± 170 ms, non-fallers: 1072 ± 109 ms) of the reaching reaction time test differentiated well between the fallers and non-fallers. The stepping reaction time test did not significantly discriminate between the two groups in the prospective study. The correlations between the laboratory and in-home assessments were 0.689 for the reaching reaction time and 0.860 for stepping reaction time. Conclusion The study findings indicate that the Kinect-based CRT tests are feasible to administer in clinical and in-home settings, and thus represents an important step towards the development of sensor-based fall risk self-assessments. With further validation, the assessments may prove useful as a fall risk screen and home-based assessment measures for monitoring changes over time and effects of fall prevention interventions.
Conference Paper
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In this paper, we investigate methods of performing automated cognitive health assessment from smart home sensor data. Specifically, we introduce an algorithm to quantify and track changes in activities of daily living and in the mobility of a smart home resident over time using longitudinal smart home sensor data. We use an automated activity recognition algorithm to recognize a smart home resident's activities of daily living from the generated sensor data, and introduce a Compare and Count (2C) algorithm to quantify the changes in everyday behavior. We test our approach using a longitudinal sensor dataset that we collected from 18 single-resident smart homes for nearly two years and study the relationship between observed changes in the sensor-based everyday functioning parameters and changes in standard clinical health assessment scores. The results suggest that we may be able to develop sensor-based change algorithms that can predict specific components of cognitive and physical health.
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The following article is a summary of the American Geriatrics Society/British Geriatrics Society Clinical Practice Guideline for Prevention of Falls in Older Persons (2010). This article provides additional discussion of the guideline process and the differences between the current guideline and the 2001 version and includes the guidelines' recommendations, algorithm, and acknowledgments. The complete guideline is published on the American Geriatrics Society's Web site (
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Environmentally embedded (nonwearable) sensor technology is in continuous use in elder housing to monitor a new set of 'vital signs' that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to healthcare staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or healthcare providers. Discovering these new functional status 'vital signs', developing automated methods for interpreting them, and alerting others when changes occur have the potential to transform chronic illness management and facilitate aging in place through the end of life. Key findings of research in progress at the University of Missouri are discussed in this viewpoint article, as well as obstacles to widespread adoption. © 2014 S. Karger AG, Basel.
Conference Paper
The purpose of rehabilitation is to improve the movement of stroke patients so that they can independently perform daily tasks on their own. Conventional therapy is a tedious exercise which usually reduces the motivation of the patient to do the exercises. Video game is a way that can motivate and help patients recover their motor skills, and also act as a deterrent pain. Video games have been used in physiotherapy, occupational therapy, and psychotherapy. In order to increase mental satisfaction and physical vitality in rehabilitation therapy, some therapists have tried to use the off-the-shelf digital game devices in rehabilitation and have found effective treatment outcomes in addition to enhancing patients' treatment motivation. Although many studies have been done in this field, there is still lacking the existing recovery game, because too many different demands must be met for each type of therapy. There is one disadvantage at the beginning of the game because patients have difficulty understanding how to use it. The current recovery games are less fun and participative for the user, while the popular games have fewer components to use in rehabilitation training. Furthermore, post-stroke patients find it difficult to get a friend to play or spend time with them every day, which will make them feel isolated. Therefore, rehabilitation exercise games that support multi-player will provide a higher motivation than the single-player. Since most stroke patients suffer from cognitive impairment, cognitive challenge levels are also the key factors in the design of the game so that it does not become an obstacle for the recovery process. Therefore, this study develops a prototype of a rehabilitation exercise game that contains aspects of the social context, the type of movement and cognitive challenges. It also provides usability in game design, according to a post-stroke stage so that they can perform recovery activities based on their ability. In addition, this study highlights technology and rehabilitation exercise games in Malaysia.
Key findings: Residents of residential care communities are persons who cannot live independently but generally do not require the skilled care provided by nursing homes. There were 835,200 current residents in residential care communities in 2014 (1,2). "Current residents" refers to those who were living in the community on the day of data collection (as opposed to the total number of residents who lived in the community at some time during the calendar year). This report presents national estimates of selected characteristics of current residents in 2014 and compares these characteristics by community bed size. State-level estimates for these characteristics are available online at: http://
This study investigates whether motion density maps based on passive infrared (PIR) motion sensors and the average time out and average density per hour measures of the density map are sensitive enough to detect changes in mental health over time. Within the sensor network, data are logged from PIR motion sensors which capture motion events as people move around the home. If there is continuous motion, the sensor will generate events at 7 second intervals. If the resident is less active, events will be generated less frequently. A web application displays the data as activity density maps showing events per hour with hours on the vertical axis and progressive days on the horizontal axis. Color and intensity provide textural indications of time spent away from home and activity level. Texture features from the co-occurrence matrix are used to capture the periodicity pattern of the activity (including homogeneity, local variation, and entropy) and are combined with the average motion density per hour and the average time away from home. The similarity of two different density maps is represented by a number that is computed in feature space as the distance from one map to the other, or a measure of dis-similarity. Employing a retrospective approach, density maps were compared with health assessment information (Geriatric Depression Scale, Mini Mental State Exam, and Short Form Health Survey -12) to determine congruence between activity pattern changes and the health information(20). A case by case study method, analyzed the density maps of 5 individuals with identified mental health issues. These density maps were reviewed along with the averages of time out of apartment per day per hour and average density per hour for hours at home and mental health assessment scores to determine if there were activity changes and if activity patterns reflected changes in mental health conditions. The motion density maps show visual changes in the client's activity, including circadian rhythm, time away from home, and general activity level (sedentary vs. puttering). The measures are sensitive enough, yielding averages of time out of apartment and average density per hour for hours at home that indicate significant change. There is evidence of congruence with health assessment scores. This pilot study demonstrates that density maps can be used as a tool for early illness detection. The results indicate that sensor technology has the potential to augment traditional health care assessments and care coordination.
A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this work, a single Kinect sensor and computer were deployed in the apartments of older adults in an independent living facility for the purpose of continuous, in-home gait measurement. In addition, a monthly fall risk assessment protocol was conducted for each resident by a clinician, which included traditional tools such as the timed up a go and habitual gait speed tests. A probabilistic methodology for generating automated gait estimates over time for the residents of the apartments from the Kinect data is described, along with results from the apartments as compared to two of the traditionally measured fall risk assessment tools. Potential applications and future work are discussed.
Introduction: There is a critical need for public health interventions to support the independence of older adults as the world's population ages. Health smart homes (HSH) and home-based consumer health (HCH) technologies may play a role in these interventions. Methods: We conducted a systematic review of HSH and HCH literature from indexed repositories for health care and technology disciplines (e.g., MEDLINE, CINAHL, and IEEE Xplore) and classified included studies according to an evidence-based public health (EBPH) typology. Results: One thousand, six hundred and thirty-nine candidate articles were identified. Thirty-one studies from the years 1998-2011 were included. Twenty-one included studies were classified as emerging, 10 as promising and 3 as effective (first tier). Conclusion: The majority of included studies were published in the period beginning in the year 2005. All 3 effective (first tier) studies and 9 of 10 of promising studies were published during this period. Almost all studies included an activity sensing component and most of them used passive infrared motion sensors. The three effective (first tier) studies all used a multicomponent technology approach that included activity sensing, reminders and other technologies tailored to individual preferences. Future research should explore the use of technology for self-management of health by older adults; social support; and self-reported health measures incorporated into personal health records, electronic medical records, and community health registries.