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Diego E. Arias
Universidad de Concepción, Chile; e-mail: diegoarias@udec.cl
Esteban J. Pino
Universidad de Concepción, Chile; e-mail: epino@ieee.org
Pablo Aqueveque
Universidad de Concepción, Chile; e-mail: pablo.aqueveque@udec.cl
Dorothy W. Curtis
Massachusetts Institute of Technology, MA, USA; e-mail: dcurtis@csail.mit.edu
Wireless Monitoring System for Wheelchair
Users with Severe Mobility Impairment
Diego E Arias, Esteban J. Pino, Pablo Aqueveque and Dorothy W. Curtis
Abstract According to the World Health Organization, around the world there
are more than 785 million people with a disability. This phenomenon is produced
to a great extent by the aging of the population and the increase in chronic diseas-
es. These patients may be affected physically and/or cognitively, requiring nursing
care and/or family assistance to perform daily activities. Bringing new tools to
families and professional caregivers to improve the care of these patients is essen-
tial to increase the quality of life for both the disabled people and the caregivers.
Recent advances in sensors, wireless communication systems and information
technologies make possible the development of portable and wearable systems to
monitor mobility impaired patients continuously during daily activities. Collecting
vital signs, patient activity and ambient conditions allow the patient’s health status
to be assessed, providing an extra level of safety in cases of emergency. Also this
information is useful for clinicians to manage treatment and rehabilitation thera-
pies. However, the main challenge is to acquire this information unobtrusively,
with a minimal impact on patients’ daily life. To this end, new ways of collecting
physiological information are needed.
2 Diego E. Arias et al.
1 Introduction
People with physical or mental disabilities are impaired in one or more major life
activities. According to the World Health Organization, as of 2011, there were
more than 785 million people with a disabling condition in the world [1]. As life
expectancy increases, people are spending more years of their life with some kind
of disability, usually due to a chronic disease [2]. Depending on the type of disa-
bility, patients may be affected in their physical and/or cognitive skills, requiring
continual nursing care and assistance to perform daily activities. Usually, family
members take the responsibility for supporting and caring for them at home. As an
alternative, they can be put in a nursing home or assisted living facility under the
charge of specialized caregivers. This global scenario challenges researchers to
develop new technological solutions designed for families and/or professional
caregivers to improve the care of these patients, increasing their quality of life.
As technologies advances, new assistive devices can be designed to improve
the care of people with disabilities. Recent advances in sensors, wireless commu-
nication systems and information technologies make possible the development of
portable and wearable biomedical systems to monitor patients continuously during
daily activities through mHealth. Collecting vital signs, patient activity and ambi-
ent conditions allow the patient’s health status to be assessed, providing an extra
level of safety in cases of emergency. Also this information is useful for clinicians
because they can use it to improve treatment and rehabilitation therapies. Howev-
er, the main challenge is to acquire this information unobtrusively, with a minimal
impact on patients' daily life. For instance, conventional sensors such as skin elec-
trodes can cause dermatitis during prolonged use, producing discomfort [3]. Also,
respiration belts can be difficult to wear and uncomfortable for impaired patients.
To this end, new ways of collecting physiological information are needed.
This chapter describes a monitoring system designed for wheelchair users who
suffer severe motor disabilities. The system consists of several sensors and porta-
ble equipment deployed on a wheelchair, able to sense physiological data, patient
activity level and ambient conditions in a non-invasive way. The selected sensors
are able to capture respiration and heart activity from ballistocardiogram (BCG)
signals, seat and back pressure changes, wheelchair tilt angle, ambient temperature
and relative humidity [4,5]. This portable equipment consists of data acquisition
hardware, a central processing unit and a communication system. The information
obtained from sensors is sent to a patient monitoring center and is saved in a data-
base. The communication can be done in two ways, depending on whether the pa-
tient is in a nursing home or not. In the first case, the good connectivity and easy
implementation of a Wi-Fi network is preferred. For patients moving outside of
the nursing home (moving around in a city), data transfer using GSM or GPRS
technologies can be used. Electrical power for the portable system is supplied by
the wheelchair batteries. The sensing procedure, data computing and data transfer
are carefully designed to minimize the energy consumption.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 3
A pilot study was conducted to evaluate the system in an actual clinical envi-
ronment [6]. We analyze the results obtained during a two week evaluation to
highlight the capability of such a monitoring system. Six Multiple Sclerosis (MS)
patients, all of them permanent wheelchair users, were recruited from an assisted
living facility to use the system during daily activities. Results show the feasibility
of implementing alarms and relevant information capture to monitor wheelchair
users. The implemented system is able to provide relevant information such as
time on wheelchair, moving vs resting, environmental conditions, vital signs and
pressure relief, which are useful for guiding or warning patients and caregivers.
Also in this chapter, we describe the several processing stages used for this ap-
plication. For instance, multi-level wavelet decomposition is used to extract respi-
ration and heart activity from BCG signals, peak detection algorithms are imple-
mented to estimate heart and respiration rate, an algorithm is used to detect
pressure relief behavior based on both pressure sensors and accelerometer data,
patient activity level is measured during the day and a heat index based on tem-
perature and humidity to avoid heat exposure is calculated.
1.1 Chief Problems for Mobility Impaired People
Pressure ulcers (PU) are injuries on the skin produced by excessive pressure dur-
ing long periods of time. Excessive pressure, usually over bony prominences, re-
duces the blood flow producing tissue ischemia and death, resulting in an ulcer.
Wheelchair users spend long hours sitting, making them particularly prone to de-
veloping PU. To avoid PU, a person has to change position regularly. This way,
the excessive pressure is relieved and blood can flow into the previously com-
pressed tissue. Clinicians recommend that patients perform push-ups, side-to-side
leans or lean forward over the knees as methods to relieve pressure. However,
people who suffer severe mobility impairment cannot relieve pressure by them-
selves. In severely impaired people, pressure relief motions need assistance, from
either family members, specialized caregivers or powered systems. Powered
wheelchairs equipped with seat functions such as a tilt-in-space are usually pre-
scribed in these cases. The tilt-in-space system allows the seat angle orientation to
be changed in relation to the ground while maintaining the seat to back angle [7].
This mechanism redistributes the seat pressure, transferring it from the seat to the
back.
However, the use of a tilt-in-space system depends on its user as well as care-
givers. It is a conscious, manual activity, whose importance must be clear to those
in charge of practicing it. Usually, people need training and/or reminders to tilt the
wheelchair. Furthermore, some patients such as those with Spinal Cord Injury
(SCI) and MS can suffer loss of awareness, sensory impairment and cognitive loss
which can result in forgetting to tilt their wheelchairs [8,9]. Also, the high work-
load in nursing homes can lead to caregivers forgetting to tilt the patient's wheel-
chair or reminding patients to tilt their chairs. The same can happen when a family
4 Diego E. Arias et al.
member is responsible for the patient's care. In those cases, it would be valuable to
be able to alert the patient and/or the caregiver when such action is necessary.
Under severe mobility impairment, people can develop comorbidities related to
respiratory and cardiovascular function. For instance, people with MS can suffer
respiratory muscle weakness, bulbar function impairment and complications in
breathing control which produce difficulty in breathing [10]. Also, the most severe
cases of SCI are unable to breathe due to paralysis of respiratory muscles requir-
ing mechanical ventilation support. Furthermore, this population presents prob-
lems in the autonomic nervous system, in charge of cardiovascular functions regu-
lating the heart and blood vessels. Autonomic dysfunction increases the
probability of suffering from heart diseases [11,12]. These examples illustrate why
respiratory and cardiovascular function variables should be monitored. Vital signs
such as heart rate and respiratory rate provide information related to health status
and they have been reported as predictors of cardiovascular and respiratory failure
[13]. Also, measuring vital signs is an important part of the nursing assessment,
therefore monitoring this information continuously will facilitate this task and help
prevent further complications.
Some patients can also suffer thermoregulation problems related to the heat.
Particularly, most MS patients are very sensitive to heat, producing an exacerba-
tion of symptoms such as fatigue [14]. Also, SCI patients are affected by heat due
to impaired innervation of sweat glands and cutaneous blood vessels [15] reducing
their ability to dissipate heat. For this reason, caregivers need to supervise patients
when they are outside. Long periods of time exposed to heat without caregiver’s
supervision can produce dehydration and sunburns. Deaths have been reported in
nursing homes produced by heat exposure due to an impaired self-awareness and
failure in supervision. Also, patients in their homes have been found dead due to
heat exposure in situations like sunbathing [16] and hot baths [17] without super-
vision.
1.2 Relevant Physiological Variables
To assess the physiological state of a person with mobility impairments, the rele-
vant variables are the ones related to their main problems. In this case, the main
concerns are: PU, activity level, cardio-respiratory problems and heat stress.
1.2.1. Pressure Ulcers
To avoid PU, the variable to be measured is time between tilts and the tilt angle, to
ensure a proper pressure relief event. Maximum recommended time between tilts
varies [18,19], but should be somewhere between 15 minutes and 2 hours. There
are different visions as to what constitutes a proper tilt. Depending on the source,
the recommended minimum tilt angle can vary between 25° up to 65° from the
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 5
horizontal (regular) sitting position. A safer approach is to combine information
from tilt angle change and pressure distribution on the chair. A change in angle
accompanied by a corresponding change in pressure distribution is a clear indica-
tor that a proper pressure relieving tilt has been accomplished.
1.2.2. Activity level
The activity level of a subject can be a good predictor of their general health. A
sudden decrease in activity in consecutive days can be an indicator of a deteriorat-
ing condition, such as an increasing pain level, fatigue exacerbation or a bout of
depression [20]. Activity level is also related to PU and cardio-respiratory issues.
1.2.3. Cardio-respiratory problems
The main variables to monitor cardio-respiratory condition are Heart Rate (HR)
and Respiratory Rate (RR). Also, with good measurements, it is possible to detect
dangerous events such as arrhythmias and apneas. To detect arrhythmias, the
physiological variable measured must somehow contain information regarding the
heart activity. Several non-invasive approaches exist, such as capacitive coupling
ECG, sensors embedded into clothing, accessories or through ambient sensors
embedded in everyday objects [21]. Contactless ECG is prone to artifacts from
movement and external electromagnetic interference. An alternative modality for
heart activity is BCG, a mechanical signal produced by the ejection of blood from
the heart [22]. While still sensitive to movement artifacts, it has more immunity
from electromagnetic sources. The respiratory signal also can be acquired from
multiple modalities, such as movement, air flow or blood gas measurements [23].
The least invasive is this case is pressure-based, detecting the thoracic movement
to derive respiration rate. Even though this method does not provide information
related to the actual gas exchange, it is sufficient to detect respiratory stress, such
as tachypnea and apnea.
1.2.4. Heat stress
There are several recommendations regarding the dangers of high temperature and
humidity combinations. The usual indexes related to heat stress are Heat Index
(HI) and Dew point. Ambient temperature (TAMB) and relative humidity (RH) are
used to calculate the dew point value and the HI. These indexes provide infor-
mation of heat exposure and its possible effects on human body. They correspond
to two methods to assess the same phenomenon. The dew point is a measure of the
amount of moisture in the air. It is defined as the temperature to which air must be
cooled in order to reach saturation. The dew point value has been described as a
possible indicator of exacerbation of the symptoms produced by heat in MS pa-
6 Diego E. Arias et al.
tients [24]. High dew point values increase the probability of the symptoms wors-
ening, because it becomes increasingly difficult to evaporate sweat and regulate
body temperature. A dew point over 17 °C is reported to have severe effects on
MS symptoms [25].
Heat index or apparent temperature is an index to quantify how hot the body
feels when it is exposed to an environment with high temperature and humidity.
As with the dew point, in high humidity conditions sweat evaporation is reduced,
producing the sensation of being overheated. The HI value is calculated using the
equation presented in [26], for RH greater than 40%. Table 1 summarizes the rec-
ommendations regarding the effect of HI in high risk group people.
Table 1 Heat Index effect on people in high risk group.
Heat Index
(°C) General effect
Above > 54 HIGHLY LIKELY heat or sunstroke with continued exposure
41-54 LIKELY sunstroke, heat cramps or heat exhaustion. POSSIBLE
heatstroke with prolonged exposure
32-40 POSSIBLE sunstroke, heat cramps or heat exhaustion with pro-
longed exposure and/or physical activity
26-31 POSSIBLE fatigue with prolonged exposure and/or physical ac-
tivity
1.3 Monitoring Biomedical Variables
A monitoring system for wheelchair users should be based on unobtrusive sensors,
combined with a wireless link to relay alerts and possibly data logging to a central
station. Since the system's priority is to enhance patient's quality of life, both at
home or in a nursing home, it is recommended that the sensors are installed into
the wheelchair, not requiring skin contact with the user. Data is then collected and
analyzed locally in a netbook computer or equivalent low-power device with wire-
less connectivity to be able to alert a central station. The whole system can be
powered from the electric wheelchair batteries. A general system diagram is
shown in Fig. 1. As mentioned, the relevant variables for mobility impaired people
are time between tilts, activity level, physiological variables such HR and RR, and
ambient temperature and humidity. Monitoring those variables requires special-
ized sensors.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 7
Fig. 1 General system diagram. Unobtrusive sensors on the wheelchair collect data and send
them to a netbook for local processing and storage. Wireless capability allows relaying relevant
information to a central station.
1.3.1. Time between tilts
An accelerometer attached to the tilting structure of the wheelchair is able to
measure angle to the horizontal plane by analyzing the g-force vector direction in
static condition. Instead of measuring the actual tilt angle, to accommodate all
wheelchair models and avoid complicated calibration procedures, it is preferable
to monitor angle variations rather than absolute values.
1.3.2. Activity level
The same accelerometer used for angle measurement serves as activity monitor.
Activity is easily detected with the accelerometer. During wheelchair movement,
the accelerometer shows forces in multiple directions and with varying amplitudes
mainly due to floor imperfections.
1.3.3. Heart and Respiratory Rate
Heart and respiratory rates are measured with unobtrusive pressure sensors. High-
ly sensitive pressure sensors based on Electromechanical Films can detect BCG
signals, usually mixed up with respiration signals. Less sensitive pressure sensors
such as Force Sensing Resistors (force/resistance transducers) are also able to rec-
ord respiration signals. In that case, BCG signals are too small to produce a no-
ticeable variation. Both sensor types allow unobtrusive monitoring, by installing
them in furniture that is used daily, such as beds or wheelchairs.
8 Diego E. Arias et al.
1.3.4. Temperature and Humidity
There are many commercially available ambient sensors that report temperature
and humidity in both analog and digital format. As an ambient sensor, it can be in-
stalled in any convenient location.
2 Proposed System
Based on the general requirements for an assistive device for mobility impaired
people, four prototypes were built on electric powered wheelchairs. The imple-
mented system is shown in Fig. 2.
Fig. 2. Implemented system on electric powered wheelchair, (a) front and (b) back view. Unob-
trusive sensors are installed on the wheelchair, under backrest foam and pressure relief cushion
on the seat. Ambient sensor and accelerometer are attached to wheelchair structure, away from
the user. The system is powered from the wheelchair batteries.
2.1 Sensor Selection and Acquisition Circuits
The prototypes carry an assortment of sensors deployed unobtrusively on the
wheelchair to monitor vital signs, pressure relief behavior, patient activity and
ambient conditions. To facilitate system acceptance, the key is to minimize impact
in patient's daily life. Therefore, the selected sensors are able to capture all the in-
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 9
formation in a non-invasive way avoiding discomfort to the patient. Accordingly,
the following sensors were selected: 3-axis accelerometer, EMFi, FSR, tempera-
ture and humidity sensor.
2.1.1. Accelerometer
The ADXL335 is a 3-axis accelerometer with ± 3g measurement range and analog
output data. It is used to measure wheelchair tilt based on the static acceleration of
gravity and detect wheelchair vibration, crashes and movement artifacts based on
dynamic acceleration.
2.1.2. Electromechanical Films (EMFi)
An EMFi is a very sensitive sensor which can measure small pressure variations.
EMFi consists of a film structured with flat voids separated by thin polyolefin lay-
ers. According to the sensor structure, EMFi sensors respond better to thickness
change rather than strain. Thickness changes in the EMFi sensors generate a cor-
responding charge and hence, a voltage can be measured using a conditioning cir-
cuit.
Two EMFi sensors (model L-3030, 290x300 mm, manufactured by EMFIT
Ltd.) were deployed on a wheelchair to measure BCG signals. Using this infor-
mation, heart and respiratory rate can be calculated. One sensor was placed on the
seat (EMFiS), under a pressure relief cushion and another on the backrest, inside a
thin foam (EMFiB). Once the patient sits on the wheelchair, the system starts to
collect respiration and heart activity without requiring any sensors on the skin,
which is the main advantage over conventional ECG.
2.1.3. Force sensing resistor (FSR)
An FSR sensor is a polymer thick film which reduces its resistance when a force is
applied in its surface. Nine small 44x38 mm FSR sensors (model FSR 406 manu-
factured by Interlink Inc.) are deployed on the wheelchair to measure prolonged
pressure on the skin over the buttock and back area. Four sensors are put on the
seat (FSRS) and five on the backrest (FSRB). FSRB can also capture respiration ac-
tivity based on thoraco-abdominal movement.
2.1.4. SHT15 sensor
The SHT15 (manufactured by Sensirion) measures ambient conditions during out-
door and indoor activities. This information will be used to avoid dangerous heat
exposure. This sensor integrates TAMB and RH in one chip. Its digital output data
10 Diego E. Arias et al.
are sent to the microcontroller unit (MCU) using a proprietary communication
protocol.
2.2 Portable Processing Unit
A Data Acquisition Hardware (DAH) board captures the data from the different
sensors and routes them to a netbook for storage and further processing. The DAH
includes conditioning circuits for FSR and EMFi signals, an MCU to acquire, tag
and pack the data and a serial transmission stage to the netbook.
The EMFi and FSR sensors require analog conditioning circuits for signal
transduction, amplification and filtering before the analog to digital converter
(ADC). The conditioning circuit for EMFi sensors was based on [27]. It consists
of a charge amplifier followed by a second order Sallen-Key low pass filter with a
cut off frequency of 30 Hz. The circuit has calibration potentiometers to modify
offset, gain and sensor sensitivity. For FSR, the conditioning circuit is a current to
voltage amplifier followed by another 30 Hz low pass filter. The amplifier for
FSRB is sensitive enough to capture respiration signals.
The information acquired by the sensors is sent into an ATxmega128A3 MCU
running at 12 MHz. The main tasks of the MCU are sampling the signals from the
ADC, establishing the SHT15 communication protocol and sending all the infor-
mation to a netbook for further processing and storage. Each analog signal is sam-
pled at 100 Hz using 11 bits of resolution. Ambient data from SHT15 sensor is
read digitally using a two-wire serial protocol. TAMB and RH are sampled at 0.1 Hz
and their resolutions are 12 bits and 8 bits respectively.
Each sensor datum transferred to the netbook has 24 bits: 12 bit of data and 12
bit for a unique identifier used to unpack the data. There are 16 identifiers, one for
each measured variable. The MCU sends the data to a netbook installed on a sub-
ject's wheelchair via serial communication at 57.6 Kbps. The devices are connect-
ed to each other by a RS-232 / USB adapter.
2.3 Power Supply
The system is fed from a 24V battery located under the wheelchair (Fig. 2). Nor-
mally, the 24V are composed of two 12V batteries with up to 70Ah. The battery
voltage has a good regulation when the wheelchair is in use. DC Motors for oper-
ating the wheelchair are the main load and consume several amps without affect-
ing the voltage when running. It is not necessary to install additional equipment
(filters) to protect the electronic circuits against transients or voltage fluctuations
on the batteries voltage. A schematic circuit is shown in Fig. 3.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 11
The power consumption of the monitoring system must be low enough so as to
not affect the wheelchair autonomy. The estimated power consumption of the
whole
Fig. 3 Monitoring system power supply draws power from the electric wheelchair batteries. Mul-
tiple voltage outputs are necessary for different subsystems.
system is shown in Table 2. In the prototype system, the highest power consumers
are the netbook (24W) and the power supply (8W). A total consumption of almost
33W is obtained. In a new design, the netbook was replaced by a Raspberry PI®
system that consumes only 3.5W with Wi-Fi communication. In this case, the
power supply consumption is reduced to 1.4W to energize the whole system,
yielding over 80% power reduction.
The power supply uses switching mode voltage regulators. These regulators
have efficiencies between 70% and 90% when the voltage is regulated from 24 V
to 3.3V and 5V. The low voltage required by the electronic circuits requires the
use of Buck converters. Switching converters can affect communications systems
because they generate electronic noise (EMI / RFI). For this reason, the power
supply must be encapsulated in a Faraday cage.
2.4 Communications Systems, Data Transfer and Databases
The acquired data is stored and processed in the netbook mounted on the powered
wheelchair. A Python script reads the raw data sent by the MCU through the serial
port and writes it to a local PostgreSQL database [28] in the netbook. Another
script unpacks the data according to the identifiers. The netbook will be used to
generate alarms and patient recommendations in a stand-alone configuration and
to send the data to a nursing station for caregiver’s alarms in the final version.
The real time conditions envisioned for this project are 5:
1 Normal condition
2 Inadequate pressure relief
12 Diego E. Arias et al.
3 Excessive heat exposure
4 Abnormal HR (high or low)
5 Abnormal RR (high or low)
Table 2 Power system consumption.
Device Description Voltage
(V)
Power con-
sumption (W)
Netbook and
communication
A netbook with Wi-Fi communications.
In the new design, this equipment is re-
placed by a Raspberry PI®
20V 24W
Power supply
Isolated and unisolated DC-DC convert-
ers. A switching mode power supply is
recommended to increase the efficiency
of the power system.
24V 8W
Analog circuits Sensors and analog circuits for data ac-
quisition and conditioning ±5V 1W
MCU
An ATxMEGA128 MCU is used. The
consumption includes AD converters,
serial communications and full 12MHz
working clock.
3.3V 60mW
Total 33W
Conditions 1 to 3 can be communicated directly to the wheelchair user, as
voiced recommendations (“Please tilt your wheelchair”) or alarms (“There is risk
of heat stroke. Go back to a cooler location”). Depending on the situation, some or
all of these alarms can be forwarded to a caregiver or family member via email
notification, SMS messages or through pop-up windows in the nursing station.
Data from the netbooks is sent to the central station with maximum secrecy. No
personal data is sent, only an arbitrary wheelchair identifier which can only be de-
identified by authorized personnel (or relatives) at the server. Furthermore, all data
communication is encrypted using ssh tunnels and public/private encryption keys,
readily available in all modern operating systems.
At the central station, all data is logged into a main database. Here, secondary
data processing occurs, which does not require real time performance. At this
stage, long term trends are analyzed, such as activity level in the last 7 days, tilt
compliance or dangerous exposure to heat. This also allows a stand-alone system
to upload its information once a day if necessary. In the case of assisted living fa-
cilities or nursing homes with many residents, this also serves as a way to avoid
overloading the wireless network during the day, when it may be in use for other
purposes.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 13
2.5 Signal Processing and Algorithms
After acquiring the raw data, several algorithms extract relevant information re-
quired to alarm the user, the caregivers or for off-line analysis and decision sup-
port.
2.5.1. Wavelet decomposition
Raw EMFi signals are filtered to extract respiration and heart activity. To this end,
a filter bank is implemented based on the Discrete Wavelet Transform (DWT).
The filter bank allows decomposing the signals in n levels using low-pass and
high-pass filters designed with a specific mother wavelet. The original signal is
decomposed in details (Di) and approximations (Ai), where i is i-th decomposition
level. Di and Ai correspond to high and low frequencies, respectively. EMFiB and
EMFiS are decomposed in 6 levels using a symlet 5 and daubechies 10 as mother
wavelets, respectively. The mother wavelets were selected based on the similarity
with the clean BCG waveforms from each sensor. Fig. 4 shows an example of
wavelet processing. The raw EMFIB is decomposed into A6 and D1-D6 levels. Fig.
5 shows BCG and respiration signals obtained from the selected Di and Ai. Respi-
ration signals are reconstructed using A6 and BCG signals are reconstructed add-
ing the details D3, D4 and D5.
2.5.2. Respiratory Rate Algorithm
An algorithm for respiratory rate calculation was implemented on the respiration
signal extracted from raw EMFi data. The algorithm is based on [29] and [30]. It
detects inspiration and expiration cycles from respiratory waveform using a
smoothed version of the original signal and extracting features such as area, width,
maximum and minimum of each cycle. Once the features are calculated for both
an inspiration and expiration cycle, the algorithm moves on to a decision stage to
discriminate valid respirations from artifacts produced by movements. Two con-
secutive valid breaths are required to calculate RR.
2.5.3. Heart Rate Algorithm
An algorithm for heart rate calculation was implemented using BCG signals ex-
tracted from raw EMFi data. The algorithm consists of looking for possible beats
in zones of BCG signal with high energy. Energy of BCG signals was calculated
using a 30 ms sliding window and a peak detection algorithm was implemented to
detect high energy peaks. When a peak is detected, a window of 700 ms is
scanned in the BCG signal to detect potential beats. Maxima and minima of the
14 Diego E. Arias et al.
BCG signal inside this 700 ms window are identified and the distance between
consecutive peaks is calculated. The segment with the greater distance corre-
sponds to a beat.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 15
Fig. 4 Wavelet decomposition of raw data from EMFiB into A6 (approximation) and D1-D6 (de-
tails).
16 Diego E. Arias et al.
Fig. 5 Wavelet recomposition of BCG and respiration signal from selected components of the
raw data.
2.5.4. Pressure Relief Detection
A pressure relief by tilt (PRT) occurs when a wheelchair uses its tilt-in-space ca-
pability to produce pressure relief over the buttocks area. According to the study
presented in [31], pressure relief is achieved if the skin is unloaded for at least
3:30 minutes, enough for the tissue to be oxygenated. For increased safety, in this
project, a threshold of 5 minutes is used.
The PRT detection algorithm uses the accelerometer combined with the pres-
sure sensors data. The reported angle is averaged using a 10 second sliding win-
dow to detect changes. When the tilt angle decreases by at least 5 degrees, which
means that the wheelchair is tilted back, and the pressure sensors reduce their level
sharply, the tilt is marked as possible PRT. To consider the tilt as an actual PRT,
the subject must maintain this position or tilt the wheelchair even further for at
least 5 minutes. Otherwise, the algorithm is restarted. When the subject returns to
the initial position, the tilt is finished. In Fig. 6, a full day record shows 4 PRT de-
tections. PRT are detected from the onset (▼) to their end (□). It is possible to ob-
serve how the pressure on the seat is transferred to the back during a PRT (Fig. 6
a, b). The pressure sensors are also used to determine wheelchair occupancy and
for user activity estimation. In Fig. 6 the volunteer is on the wheelchair for 6:41
hours.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 17
Fig. 6 PRT detection algorithm. (a) FSRB, (b) FSRS and (c) tilt angle obtained from the accel-
erometer. Time between ▼ and □ marks indicate a valid PRT. The beginning and end of wheel-
chair occupancy is also deduced from the pressure sensor data.
2.5.5. Activity Level Estimation
An Activity Index (AI) was introduced to quantify the activity level of each sub-
ject during the time spent on the wheelchair. AI is defined as the time showing
high activity divided by the total wheelchair occupancy time. The AI estimate is
based on the variations of the accelerometer data which reflect the subject’s
movement and the wheelchair vibration during driving. To extract this component,
the standard deviation of the accelerometer data is calculated using a sliding win-
dow. High standard deviation values indicate high activity segments. A fixed
threshold of 0.5 was used to distinguish between high and low activity, as shown
in Fig. 7.
18 Diego E. Arias et al.
Fig. 7 The accelerometer data (top) is used to estimate the activity level for each subject. High-
lighted segments show high activity periods (e.g. driving, tilting or moving). When the standard
deviation of the accelerometer data (bottom) is low, there is no activity (e.g. resting, wheelchair
stopped).
2.6 Common issues
Raw EMFi signals are affected by artifacts due to a patient's movement and
wheelchair vibration during driving. When this occurs, despite the filtering pro-
cess, the artifacts hide the underlying signal completely, rendering the RR and HR
algorithms useless. For this reason, a noise detection stage is implemented in each
algorithm to suspend its execution when noisy signals are detected.
The noise detection stage uses the activity level estimation presented in 2.5.5.
When high activity is detected, the algorithms are suspended until low activity is
detected again. The noise detection stage is also complemented by analyzing raw
EMFi data to discard saturated signals produced by sensor disconnections and
movements that are not captured by the accelerometer. Signals are considered sat-
urated if the maximum value found in a 5 second window is equal to 2n, where n is
the resolution of the AD converter used for signal acquisition (in this case, 11
bits). Fig. 8 shows how the noise detection stage works. During high activity or
when the signal is saturated, the algorithm to detect RR and HR is not running.
The FSR sensors are also used to stop the algorithms when the subjects are out of
the wheelchair or when the subjects do not lean against the sensors placed on the
wheelchair backrest.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 19
Fig. 8 When noisy segments are detected, RR and HR algorithms are suspended. Peak detection
resumes once the signal returns to normal levels
A second issue detected is wireless network failure due to wandering out of
coverage while on Wi-Fi or network downtime. In this case, local storage capabil-
ity proves useful. Once the network is re-established, data forwarding can be re-
sumed. It is also advisable that the system is able to work in a stand-alone mode,
without network connectivity. Most of the powered wheelchair users are able to
take actions to overcome a situation. It can be expected that tilting reminders and
some high HI alarms are going to be the most common events. In those cases, a
stand-alone system is sufficient to remind or request some action from the user.
Other data can be saved locally for off-line analysis or forwarded to the central
station at the earliest convenience.
3 Experience with MS Patients: A Pilot Study
Volunteers for a pilot study were recruited from The Boston Home (TBH), a spe-
cialized care residence for people with advanced MS and other neurological dis-
eases. The inclusion criteria were full time wheelchairs users with severe mobility
impairment, using electric-powered wheelchairs equipped with a tilt-in-space sys-
tem and a pressure relief cushion.
20 Diego E. Arias et al.
3.1 Study protocol
The prototypes are installed on the wheelchairs at the beginning of the study. Eve-
ry day the prototypes are checked to detect and correct any technical issues before
the volunteers start to use their wheelchairs. Prior to the data collection, the partic-
ipants are asked if the sensors deployed on the wheelchair's backrest and/or seat
produce discomfort. Then, the prototypes start to collect data continuously during
the whole time the patient spends on the wheelchair. As the data collection is per-
formed in a nursing home, researchers contact the nursing staff in case of any
problems.
To validate the information obtained from the BCG signals, once a day the res-
idents were requested to use a Nonin® finger clip pulse oximeter to acquire HR
and SpO2 level. This sensor was used during short periods of time to minimize
discomfort to the residents. This protocol was approved by The MIT Committee
on the Use of Humans as Experimental Subjects (COUHES).
3.2 Activity Reports
Table 3 shows the wheelchair occupancy and the activity level from all subjects.
On average, the participants spent 6:23 ± 1:43 hours per day in their wheelchair.
The maximum wheelchair occupancy registered was 10:36 hours and the mini-
mum was 2:11 hours. The distribution of wheelchair occupancy for all subjects is
shown in Fig. 9. A total of 39 days of data were collected. The subjects presented
an Activity Index (AI) level under 10% in 8 days (31.8%). Most cases (29 days),
the subjects presented an AI between 10% and 30%. Only on 2 days (2.2% of
time) did the subjects present an AI greater than 30%. Fig. 10 shows the AI level
and the wheelchair occupancy registered for S1 during 9 days. S1 occupied his/her
wheelchair on average 7:28 ± 0:55 hours per day and the AI level was between
20% and 30% during the 88.9% of monitored days, except for day 3 where the AI
level reached 48%. The increase in the AI is coincides with the day when S1 left
the nursing home to do some errands.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 21
Fig. 9 Wheelchair occupancy distribution from all subjects during the study days.
Table 3 Wheelchair occupancy and the activity level from all participants.
Subject
(S)
N° study
days
Wheelchair occupancy Activity index
[hh:mm]
[%]
min max mean ± sd min max mean ± sd
S1 9 6:36 9:28 7:28 ± 0:55 20.4 48.5 26.8 ± 8.7
S2 11 4:23 6:57 5:27 ± 0:51 8.5 27.3 13.7 ± 5.3
S3 6 2:10 5:06 4:11 ± 1:08 14.7 29.9 23.2 ± 5.8
S4 5 5:22 10:35 8:03 ± 2:09 7.6 61.0 21.0 ± 22.5
S5 4 3:55 4:36 4:19 ± 0:17 8.0 24.0 15.3 ± 8.5
S6 4 5:42 9:11 7:52 ± 1:30 5.0 10.0 7.5 ± 2.1
22 Diego E. Arias et al.
Fig. 10 Wheelchair occupancy and AI level registered by S1 during 9 days of study.
3.3 Tilt Compliance
There are several clinical recommendations about the frequency pressure relieving
maneuvers. Some guidelines for prevention of PU recommend performing a pres-
sure relief every 15 minutes [18], other guidelines recommend longer intervals
such as every 1 hour or every 2 hours [19]. Only 2.1% of PRT performed by the
subjects meet the recommendation if the interval is every 15 minutes, 25.8% if the
interval is every 1 hour and 57.7% if the interval is every 2 hours. Fig. 11 shows a
histogram of the intervals between tilts for each subject. S1 and S2 logged the
shorter intervals. 18.9% of the time intervals for S1 and 5.1% for S2 were under 1
hour. For S1, most of the intervals (41.7%) were between 1 and 2 hours. On the
other hand, more than 50% of the intervals spent by the rest of the subjects (S2 to
S6) were longer than 3 hours, against all recommendations. The maximum was
registered by S6 reaching 93.9% of the wheelchair time in the same position (tilt
angle). The average duration in a PRT position is 1:34 hours. However, those who
perform frequent PRT spend a shorter time in a tilted position than the subjects
who don’t. On the other hand, S6 spent around 4:30 hours in a tilted position.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 23
Fig. 11 Time intervals with absence of PRTs
3.4 Vital signs monitoring
Fig. 12 shows HR and RR trends obtained from one of the volunteers when his/her
wheelchair was stopped. This information is useful to improve patient's care
through assessing their vital signs continuously to quickly assist in case of emer-
gency. Also, measuring vital signs allows knowing patients normal ranges which
can be different from healthy people due to their impaired conditions.
During data collection, it is usual to observe the residents resting in their
wheelchair or taking a nap. Fig. 13 shows an EMFi record of volunteer S3 while
taking a nap, where it is possible to detect an 18 s apnea, condition that would be
very difficult to detect otherwise.
24 Diego E. Arias et al.
Fig. 12 HR (a) and RR (b) as detected by the algorithms. Red lines mark zones where it was not
possible to estimate vital signs due to noise.
Fig. 13 Apnea captured by EMFi sensors. A clean BCG from the heart activity can be appreciat-
ed in (a) during the respiratory pause.
3.5 Ambient conditions
People with a high level of disability, such as MS patients, are very sensitive to
heat. For this reason, extra care is necessary when the patients spend time outside
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 25
during the summer. The implemented system allows the continuous monitoring of
ambient conditions, information that can easily be used for an alarm system.
Fig. 14 shows a wheelchair user who went outdoors for over 1 hour. In the
middle of the summer, the temperature can rise above 40 °C. Depending on the
RH, the HI and the Dew point can reach dangerous values.
Fig. 14 HI and dew point values as a wheelchair user moves outdoors after lunch, in the summer.
4 Future Directions
Current advances in sensors, networks and computer science are expanding the
possible range of applications in mHealth that can be implemented. The monitor-
ing system presented here for severely impaired people is just one possible appli-
cation, but we believe it is headed in the right direction. We feel that the main
goals in future mHealth developments should be: 1) unobtrusiveness, 2) perva-
siveness and 3) robustness.
In order to provide supporting technologies for better quality of life, next gen-
eration systems should be ‘invisible’ and avoid any interference on daily living ac-
tivities. Sensors should act behind the scenes, like the pressure sensors presented
in this application. Ambient sensors, sensors embedded in everyday items such as
beds, chairs, driving wheels, etc. and/or wearable sensors, coupled with advanced
and powerful information processing systems will be able to provide just-in-time
recommendations, warnings or alarms to prevent adverse events or to improve dai-
ly living in a variety of settings.
26 Diego E. Arias et al.
Pervasiveness of future mHealth applications is very desirable to provide con-
stant monitoring and advice. The continuous drop in technology prices, or the in-
crease in technology at constant price, allows us to envision an expansion of ap-
plication settings, such as monitoring systems for independent patients with
chronic diseases at home, in schools and gyms to detect at risk people on time, at
work in adverse ambient conditions and in rural areas or in currently ‘low tech’
health centers to provide better diagnostic tools and decision support. This possi-
ble physical expansion of mHealth applications will provide basic health screening
capabilities in non-standard settings, decreasing patient requirements for treatment
in traditional health centers. Again, using the system presented as an example,
pervasively monitoring physiological variables and general activity information
would bring valuable insight to people with disabilities for deciding whether an
appointment with a physician is due or an emergency visit to the hospital is re-
quired. Even during the visit, the pervasiveness of the system can prove useful by
providing a large amount of previously recorded health data to assist the caregiver
taking a prompt and correct action based on a correct diagnosis.
To support these advances, more research is necessary in algorithm robustness.
Unobtrusive and pervasive systems mean obtaining data and making decisions
based on low quality information. Clearly, as seen in our pilot study, there are cer-
tain conditions where the system is unable to properly monitor the physiological
variables due to the noise level. Even after filtering out the intervals detected as
noisy, the RR and HR series present errors. Better noise detection algorithms
combined with better signal processing techniques will help making mobile moni-
toring systems more robust in the future.
5 Discussion and Conclusion
The proposed system allows the capture of physiological data without causing dis-
comfort to the patient. The system is set up on a wheelchair by deploying unobtru-
sive sensors in a pillow or a piece of foam and under a pressure relief cushion to
avoid direct contact. This way, patient stress is kept to a minimum, and the system
can be used to capture relevant information during long-term monitoring. These
features are key for patient acceptance.
However, the main problem for monitoring and assessing vital signs in daily
life is noise. EMFi sensors which were used to measure BCG signals and then ob-
tain HR and RR trends, are very sensitive. This sensitivity, which is required to
detect small signals such as BCG, render the system useless in noisy conditions
such as when the wheelchair is moving or the patient is changing positions. To
achieve more accurate results in the RR cycle detections, a signal quality estima-
tion algorithm is essential to decide when the algorithms will perform adequately.
However, during periods of low activity, the algorithms are able to provide valid
information regarding main physiological variables such as RR and HR.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 27
The implemented system can also capture pressure changes and tilt usage in a
non-invasive way, continuously during daily activities. Combining the data ac-
quired from FSR sensors and the accelerometer, it is possible to detect when the
patients relieve pressure by tilting. Analyzing the information collected by our
system, useful parameters are obtained such as wheelchair occupancy, activity
level, PRT per day, time spent in a tilted position, intervals without performing a
PRT, etc., which can be useful for providing objective information to clinicians
about pressure relieve behavior.
The results also show that the subjects did not relieve pressure as recommended
using the tilt-in-space system. They spent long periods (over 3 hours) without per-
forming a PRT. In addition, during the study, there were entire days when the sub-
jects did not perform PRT. If these situations recur frequently and the subjects
spend long hours on the wheelchair without relieving pressure, the probability of
developing PU increases.
An interesting unintended finding was the apnea detection. Caregivers at the
living facility also showed interest in evaluating this particular system with regard
to sleep apnea, mainly because polysomnography studies are particularly compli-
cated for this group of people. A reliable apnea screening system helps improve
the quality of care delivered to their residents.
The pilot study also confirmed the need and the feasibility of implementing
personalized alarms or reminders for different aspects of activities of daily living
that may lead to danger or health complications. It is also clear that this system
can be readily used in a variety of settings such as home care, nursing homes
and/or assisted living facilities.
Acknowledgments The authors would like to thank to the MIT International Science & Tech-
nology Initiatives and the Biomedical Engineering program at Universidad de Concepción. The
authors would like to express our gratitude to Professor Seth Teller and the Robotics, Vision and
Sensor Networks (RVSN) for his advice and support. We also would like to thank Don Fredette
and The Boston Home for being so enthusiastic about this project.
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Keywords
Pressure ulcers, vital signs, heat stress, powered wheelchairs, monitoring system.
Questions and Answers
This system needs to connect any sensor to the patient to collect physiological da-
ta?
Ans. - No, this system does not connect any sensor or cable to the patient. EMFi
sensors for ballistocardiogram measurements can produce Heart rate and Respira-
tion rate data after processing. Other signals are obtained from sensors connected
directly to the wheelchair.
2. - The system can be used only inside a nursing home?
Ans. - The system uses 802.11b WiFi to communicate with the server in the nurs-
ing home. The system can be upgraded with an EDGE or GPRS mobile system to
communicate with central server from anywhere with mobile data coverage. This
way, people who live independently could also benefit from enhanced physiologi-
cal monitoring.
3. - What is the difference between EMFi and FSR measurements in this system?
Ans. - EMFi sensors have a bigger area than FSR sensors. This characteristic of
EMFI sensors allows the measurement of small changes on pressure generated by
the natural movement of the body. In fact, EMFi sensors are more sensitive than
FSR to detect Heart Rate and Respiratory Rate. FSR are used for detecting chang-
es in position of the patient and in estimating patient activity.
4. - The power consumption of the monitoring system reduces the autonomy of the
battery powered wheelchair?
Ans. - Using a Raspberry PI central unit to calculate and communicate with the
central server, the power consumption of the system is very low and does not af-
fect the autonomy of the wheelchair.
Wireless Monitoring System for Wheelchair Users with Severe Mobility Impairment 31
5. - Can the accelerometer be used to measure the movement of the body?
Ans. - Some authors use accelerometers to measure the natural movement of the
body. To this end, they put the accelerometers directly on the body. In this project,
no devices can be in contact with the patients. The accelerometer is attached to the
wheelchair structure to measure the tilt angles and whether the wheelchair is mov-
ing.
6. – Does the system generate alarms for the patient, the nursing personnel or
both?
Ans. - The monitoring system generates alarms to help patients locally. At the
same time, the system sends information to a central server to store information
and to alert the caregiver that something is wrong with the patient.
7. - If an alarm occurred, can the patient be located quickly?
Ans. - The actual version of the system does not have information about the pa-
tient position. However, a GPS or an indoor positioning system can be included to
the system for outdoor or indoor position reporting respectively.
