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Analytical Survey of Wearable Sensors
Abstract
Wearable sensors inWireless Body Area Networks (WBANs) provide health and
physical activity monitoring. Modern communication systems have extended this
monitoring remotely. In this survey, various types of wearable sensors
discussed, their medical applications like ECG, EEG, blood pressure, detection
of blood glucose level, pulse rate, respiration rate and non medical
applications like daily exercise monitoring and motion detection of different
body parts. Different types of noise removing filters also discussed at the end
that are helpful in to remove noise from ECG signals. Main purpose of this
survey is to provide a platform for researchers in wearable sensors for WBANs.
... In addition, it can automatically attenuate any nonlinearity that falls in the higher frequency range [199]. The magnitude response of the low-pass Butterworth filter of order can be described as [200]: ...
... The magnitude response of the low-pass Chebyshev Type I filter for > 0 can be described as [200]: ...
... The magnitude response of the low-pass elliptical filter can be described as [200]: ...
Interest continues rapidly increasing in the remote measurement of physiological signs using camera imaging technologies for clinical and biomedical applications. Physiological signs are vital parameters that indicate the internal state of the human body. Conventionally, these signs are measured using adhesive sensors and electrodes, which may cause discomfort and constrain the patient if used for long periods. These methods may also cause skin damage and infection, especially when applied to premature infants or people with sensitive skin. Thus, this project aims to explore possible remote monitoring systems based on camera imaging technologies to monitor heart rate and respiratory rate from different regions of the human body where cardiorespiratory signals can be detected. The work in this thesis relies on magnification of nearly imperceptible variations resulting from the cardiorespiratory activity of the human body, which include skin colour variations, arterial pulse motion, head motion, and thoracic and abdominal motion. A range of image and signal processing techniques are applied to extract the cardiorespiratory signal and differentiate the various forms of abnormal cardiorespiratory events.
The contributions of this thesis are towards addressing the issue of creating robust remote image–based monitoring systems when different challenges exist, including different applied regions of interest, the existence of noise and motion artefacts correlated with the signal, monitoring multiple people at a given time (up to six people), monitoring from a long distance (up to 60 metres) and long-term monitoring. The experimental results show promising performance in comparison with the reference measurements from clinical instruments, with close agreement, strong correlation and low error rate. Therefore, this thesis leads to a new perspective for remote physiological monitoring and remote sensing assessment, showing promising performance in clinical and biomedical applications.
... : WSN Applications be wearable sensors [1] attached on the body, depending upon the application requirements. ...
... Entertainment and Sports Aerospace WSN Applications be wearable sensors [1] attached on the body, depending upon the application requirements. In the field of sports, WBASN can be deployed by using a heart rate measuring sensor placed on players body to replace him/her when, the rate reaches a critical level while playing, in order to avoid the bad performance of a particular team. ...
In recent years, interests in the applications of Wireless Body Area Sensor
Network (WBASN) is noticeably developed. WBASN is playing a significant role to
get the real time and precise data with reduced level of energy consumption. It
comprises of tiny, lightweight and energy restricted sensors, placed in/on the
human body, to monitor any ambiguity in body organs and measure various
biomedical parameters. In this study, a protocol named Distance Aware Relaying
Energy-efficient (DARE) to monitor patients in multi-hop Body Area Sensor
Networks (BASNs) is proposed. The protocol operates by investigating the ward
of a hospital comprising of eight patients, under different topologies by
positioning the sink at different locations or making it static or mobile.
Seven sensors are attached to each patient, measuring different parameters of
Electrocardiogram (ECG), pulse rate, heart rate, temperature level, glucose
level, toxins level and motion. To reduce the energy consumption, these sensors
communicate with the sink via an on-body relay, affixed on the chest of each
patient. The body relay possesses higher energy resources as compared to the
body sensors as, they perform aggregation and relaying of data to the sink
node. A comparison is also conducted conducted with another protocol of BAN
named, Mobility-supporting Adaptive Threshold-based Thermal-aware
Energy-efficient Multi-hop ProTocol (M-ATTEMPT). The simulation results show
that, the proposed protocol achieves increased network lifetime and efficiently
reduces the energy consumption, in relative to M-ATTEMPT protocol.
... The decay of the resistance band accelerates with increasing order, resulting in more accurate processing results. Based on the above characteristics, the Butterworth filter is suitable for cases where the passband and stopband ripples are small, and the requirements for the transition band signal are low [65]. The frequency response of the Butterworth filter satisfies Equation (2). ...
Wearable sensors have demonstrated wide applications from medical treatment, health monitoring to real-time tracking, human-machine interface, smart home, and motion capture because of the capability of in situ and online monitoring. Data acquisition is extremely important for wearable sensors, including modules of probes, signal conditioning, and analog-to-digital conversion. However, signal conditioning, analog-to-digital conversion, and data transmission have received less attention than probes, especially flexible sensing materials, in research on wearable sensors. Here, as a supplement, this paper systematically reviews the recent progress of characteristics, applications, and optimizations of transistor amplifiers and typical filters in signal conditioning, and mainstream analog-to-digital conversion strategies. Moreover, possible research directions on the data acquisition of wearable sensors are discussed at the end of the paper.
... Other smartwatches could use different pre-processing steps and approximations of the gathered signals [59]. Moreover, the gathered signals could be noisy [60]. People could wear the devices inappropriately. ...
Early detection of COVID-19 positive people are now extremely needed and considered to be one of the most effective ways how to limit spreading the infection. Commonly used screening methods are reverse transcription polymerase chain reaction (RT-PCR) or antigen tests, which need to be periodically repeated. This paper proposes a methodology for detecting the disease in non-invasive way using wearable devices and for the analysis of bio-markers using artificial intelligence. This paper have reused a publicly available dataset containing COVID-19, influenza, and Healthy control data. In total 27 COVID-19 positive and 27 healthy control were pre-selected for the experiment, and several feature extraction methods were applied to the data. This paper have experimented with several machine learning methods, such as XGBoost, k-nearest neighbour k-NN, support vector machine, logistic regression, decision tree, and random forest, and statistically evaluated their perfomance using various metrics, including accuracy, sensitivity and specificity. The proposed experiment reached 78 % accuracy using the k-NN algorithm which is significantly higher than reported for state-of-the-art methods. For the cohort containing influenza, the accuracy was 73 % for k-NN. Additionally, we identified the most relevant features that could indicate the changes between the healthy and infected state. The proposed methodology can complement the existing RT-PCR or antigen screening tests, and it can help to limit the spreading of the viral diseases, not only COVID-19, in the non-invasive way.
... Rapid advancements in wireless communications and networking technologies [1], [2], embedded systems [3], software agent systems [4], wearable computers and sensors [5] continues to motivate the proliferation of pervasive and ubiquitous computing. In turn, progress in these domains has propelled notable advancements in smart environments [6]. ...
... It has been concluded that by using this wearable technology method, we can transmit data which has been received from the patient's body using various sensors to the caregiver (Rehman et al., 2012). Wearable sensors have been integrated which makes it easy to track the patient anytime from anywhere. ...
Purpose – The purpose of this paper is to design a human health monitoring system (HHMS) which helps in improving diagnostics at an earlier
stage and monitoring after recoup.
Design/methodology/approach – The methodology involves a combination of three subsystems which monitors the human parameters such as
temperature, heart rate, SpO2, fall and location of the person. Various sensors are used to extract the human parameters, and the data are analysed
in a computer subsystem, through Global System for Mobile Communications (GSM) and Internet of Things (IoT) subsystem; the parameters
measured are communicated to the caregiver and doctor.
Findings – Results have successfully demonstrated monitoring human temperature human temperature, heart rate, SpO2 and fall and location
continuously using the HHMS prototype. Reliability of the technique used for monitoring these parameters is assessed by Proteus Professional 8 and
LabVIEW simulators.
Practical implications – The HHMS enables long-term monitoring without any sort of interference from regular activities and allows daily health
monitoring, elderly monitoring and so on.
Originality/value – First, the proposed HHMS simultaneously monitors five human parameters. Second, unlike most monitoring systems which uses
older communication module, the proposed system is made smart using IoT. The proposed method has been made into a prototype system as
detailed in this paper. The proposed HHMS can achieve high detection accuracy. Therefore, this system can be reliably deployed into a consumer
product for use as monitoring device with high accuracy.
... It is also referred to as Body Area Sensor Networks (BASNs) or Wireless Body Area Networks (WBANs). They consist of either in-vivo sensors, i.e. implanted inside the body or wearable sensors, i.e. attached on the body, depending upon the application requirements [3]. The BAN field is an interdisciplinary area which, could allow inexpensive and continuous health monitoring with real-time updates of medical records through the internet. ...
... However, with regards to such device-to-device (D2D) communications, we notice a lack of services provisioned for this specific type of connectivity. With the recent influx of wearables in the consumer markets [4], [5], researchers recognize the boost of direct communications between such devices and their hosts. ...
The mobile devices of today evolve towards offering uninterrupted connectivity while attempting to achieve untethered mobility of their users. Further technological advances in hardware often lead to an increased data consumption. Combining these two factors, we notice that the data rates on the current Internet connections are starting to lag behind. Ultimately, we observe a mismatch between the data transfer rate requirements and the actual throughput availability. In this paper, we utilize direct links between proximate devices to help offload the large amounts of user-originated data from the conventional cellular links. The paper explores the implementation possibilities of this technology on the consumer Android devices, as well as substantiates our application development choices. The proposed approach employs the infrastructure-based connections for coordination, while most data transfers happen over the device-to-device links. This allows the developers to utilize our data offloading platform for other proximate applications.
... Each such sensor node is deployed to monitor a specific health parameter. For instance, a pulse oximeter measures the oxygen saturation level in blood and the heart rate [16], and an EKG sensor monitors and records the EKG-graph for a patient [17], [18]. ...
In critical medical emergency situations, Wireless Body Area Network (WBAN) equipped health monitoring systems treat data packets with critical information regarding patients' health in the same way as data packets bearing regular healthcare information. This snag results in a higher average waiting time for the local data processing units (LDPUs) transmitting data packets of higher importance. In this paper, we formulate an algorithm for Priority-based Allocation of Time- Slots (PATS) that considers a fitness parameter characterizing the criticality of health-data that a packet carries, energy consumption rate for a transmitting LDPU, and other crucial LDPU properties. Based on this fitness parameter, we design the constant model hawk-dove game that ensures prioritizing the LDPUs based on crucial properties. In comparison with the existing works on priority-based wireless transmission, we measure and take into consideration the urgency, seriousness, and criticality associated with an LDPU, and, thus, allocate transmission timeslots proportionately. We show that, the number of transmitting LDPUs in medical emergency situations, can be reduced by 25:97%, in comparison with the existing time-division based techniques.
... Due to their low size they are power constrained as life-time of battery strictly depends upon the size of device. Therefore, main goal in WSNs is to enhance network lifetime by efficient utilization of available energy resources [1]. There are many strategies devised for the optimization of energy consumption in WSNs. ...
Due to small size of sensor nodes deployed in Wireless Sensor Networks
(WSNs), energy utilization is a key issue. Poor channel conditions lead to
retransmissions and hence, result in energy wastage. Error control strategies
are usually utilized to accommodate channel impairments like noise and fading
in order to optimize energy consumption for network lifetime enhancement.
Meanwhile, cooperative communication also emerges to be an appropriate
candidate to combat the effects of channel fading. Energy efficiency of
cooperative scheme when applied with Automatic Repeat Request (ARQ), Hybrid-ARQ
(HARQ) and Forward Error Correction (FEC) is investigated in this work.
Moreover, the expressions for energy efficiency of Direct Transmission, Single
Relay Cooperation and Multi Relay Cooperation are also derived. In all, our
work is focused towards energy optimal communication in WSNs. Our results show
that error control strategies with cooperative schemes can significantly
enhance system performance in form of energy optimization.
... Due to their low size they are power constrained as life-time of battery strictly depends upon the size of device. Therefore, main goal in WSNs is to enhance network lifetime by efficient utilization of available energy resources [1]. There are many strategies devised for the optimization of energy consumption in WSNs. ...
Due to small size of sensor nodes deployed in Wireless Sensor Networks (WSNs), energy utilization is a key issue. Poor channel conditions lead to retransmissions and hence, result in energy wastage. Error control strategies are usually utilized to accommodate channel impairments like noise and fading in order to optimize energy consumption for network lifetime enhancement. Meanwhile, cooperative communication also emerges to be an appropriate candidate to combat the effects of channel fading. Energy efficiency of cooperative scheme when applied with Automatic Repeat Request (ARQ), Hybrid-ARQ (HARQ) and Forward Error Correction (FEC) is investigated in this work. Moreover, the expressions for energy efficiency of Direct Transmission, Single Relay Cooperation and Multi Relay Cooperation are also derived. In all, our work is focused towards energy optimal communication in WSNs. Our results show that error control strategies along with the cooperative scheme significantly enhances system performance in the form of energy optimization.
... In our previous work [1], we have briefly discussed the types of wearable sensor with their designing issues, however in this paper wireless wearable sensors for health and physical activity monitoring are reviewed with the sensor placement issues and noise reduction ECG filters. Section II presents different types of wearable sensor that are used for detection and prediction of different motion scenarios and physical activities. ...
Wearable sensors are the major part of Wireless Body Area Networks (WBANs) for provision of health care services and physical activity monitoring. In this paper, a brief survey of different types of wearable sensors presented along with sensor placement. Designing of wearable sensors for ECG and Blood pressure also discussed. Comparative study of FIR and IIR noise reduction ECG filters discussed at the end. With the help of MATLAB R2008b, different types of FIR and IIR filters designed to remove Baseline Wander Noise, Muscle Noise and Power line Interference from raw ECG signals of different subjects and have compared which filter provides best result.
Previous medical services for humans provided healthcare information using the static-based computing of space-constrained hospitals or healthcare centers. In contrast, current mobile health information management computing and services are being provided so that they utilize both the mobility of mobile computing and the scalability of cloud computing to monitor in real-time the health status of patients who are moving. In addition, data capacity has sharply increased with the expansion of the principal data generation cycle from the traditional static computing environment to the dynamic computing environment. This chapter presents mobile cloud healthcare computing systems that simultaneously leverage the portability and scalability of healthcare services. This chapter also presents the wearable computing system as an application of mobile healthcare.
Previous medical services for humans provided healthcare information using the static-based computing of space-constrained hospitals or healthcare centers. In contrast, current mobile health information management computing and services are being provided so that they utilize both the mobility of mobile computing and the scalability of cloud computing to monitor in real-time the health status of patients who are moving. In addition, data capacity has sharply increased with the expansion of the principal data generation cycle from the traditional static computing environment to the dynamic computing environment. This chapter presents mobile cloud healthcare computing systems that simultaneously leverage the portability and scalability of healthcare services. This chapter also presents the wearable computing system as an application of mobile healthcare.
This chapter aims at giving an insight into a variety of available monitoring technologies and techniques, which aim to provide solutions to the issues listed in Chap. 3. First, we start with discussing possible data collection approaches, by revealing choices of available sensors and underlying constrains. Second, we provide a summary of sensors used for data acquisition in regard to needed medical applications, revealing what relevant parameters can be derived from those sensor measurements. We then summarize what common data processing and analysis techniques are used for interpreting this data, with a special focus on machine learning approaches. Third, we derive important requirements and underlying challenges for the involved machine learning strategies and discuss possible implications for applying the different monitoring approaches. Finally, we refer to a number of established standards, which are needed to be complied with, when developing and implementing home monitoring systems for older adults.
Previous medical services for humans provided healthcare information using the static-based computing of space-constrained hospitals or healthcare centers. In contrast, current mobile health information management computing and services are being provided so that they utilize both the mobility of mobile computing and the scalability of cloud computing to monitor in real-time the health status of patients who are moving. In addition, data capacity has sharply increased with the expansion of the principal data generation cycle from the traditional static computing environment to the dynamic computing environment. This chapter presents mobile cloud healthcare computing systems that simultaneously leverage the portability and scalability of healthcare services. This chapter also presents the wearable computing system as an application of mobile healthcare.
A wireless EEG/ECG system using non-contact sensors is presented. The system consists of a set of simple capacitive electrodes manufactured on a standard printed circuit board that can operate through fabric or other insulation. Each electrode provides 46 dB of gain over a .7-100 Hz bandwidth with a noise level of 3.80μV RMS for high quality brain and cardiac recordings. Signals are digitized directly on top of the electrode and transmitted in a digital serial daisy chain, minimizing the number of wires required on the body. A small wireless base unit transmits EEG/ECG telemetry to a computer for storage and processing.
Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.
Muscle fatigue is an established area of research and various types of muscle fatigue have been clinically investigated in order to fully understand the condition. This paper demonstrates a non-invasive technique used to automate the fatigue detection and prediction process. The system utilises the clinical aspects such as kinematics and surface electromyography (sEMG) of an athlete during isometric contractions. Various signal analysis methods are used illustrating their applicability in real-time settings. This demonstrated system can be used in sports scenarios to promote muscle growth/performance or prevent injury. To date, research on localised muscle fatigue focuses on the clinical side and lacks the implementation for detecting/predicting localised muscle fatigue using an autonomous system. Results show that automating the process of localised muscle fatigue detection/prediction is promising. The autonomous fatigue system was tested on five individuals showing 90.37% accuracy on average of correct classification and an error of 4.35% in predicting the time to when fatigue will onset.
As the world's aged population grows many governments are looking to remote healthcare monitoring solutions. It is certainly cheaper to keep the elderly and infirm in their own homes rather than in aged-care facilities. However will these remote and wireless monitoring devices deliver what they promise? The medical profession needs strong evidence before they embrace such techniques. In this paper the authors describe the building of a remote monitoring prototype which uses motes, a PDA and a network management application to show that commodity-based hardware may provide one answer to the problem of remotely monitoring people in their own homes. Privacy and security issues associated with such monitoring are also discussed.
The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented.
A general method is proposed to reduce noise by combining signals. Different measurements of the same physical quantity often exhibit different noise levels in different frequency ranges. Hence, a single high-fidelity signal can be constructed by combining the low-noise parts of the signals in Fourier space. We demonstrate this method by reducing noise in the measured bead-to-bead distance in an optical tweezers setup.
This paper presents a review of wearable EEG technology: the evolution of ambulatory EEG units from the bulky, limited lifetime devices available today to small devices present only on the head that can record the EEG for days, weeks or months at a time. The EEG requirements, application areas and research challenges are highlighted. A survey of neurologists is also carried out clearly indicating the medical desire for such devices.
Human movement has been the subject of investigation since the fifth century when early scientists and researchers attempted to model the human musculoskeletal system. The anatomical complexities of the human body have made it a constant source of research to this day with many anatomical, physiological, mechanical, environmental, sociological and psychological studies undertaken to define its key elements. These studies have utilised modern day techniques to assess human movement in many illnesses. One such modern technique has been direct measurement by accelerometry, which was first suggested in the 1970s but has only been refined and perfected during the last 10-15 years. Direct measurement by accelerometry has seen the introduction of the successful implementation of low power, low cost electronic sensors that have been employed in clinical and home environments for the constant monitoring of patients (and their controls). The qualitative and quantitative data provided by these sensors make it possible for engineers, clinicians and physicians to work together to be able to help their patients in overcoming their physical disability. This paper presents the underlying biomechanical elements necessary to understand and study human movement. It also reflects on the sociological elements of human movement and why it is important in patient life and well being. Finally the concept of direct measurement by accelerometry is presented with past studies and modern techniques used for data analysis.
This paper presents the design and development of a wearable ubiquitous healthcare monitoring system using integrated electrocardiogram (ECG), accelerometer and oxygen saturation (SpO(2)) sensors. In this design, non-intrusive healthcare system was designed based on wireless sensor network (WSN) for wide area coverage with minimum battery power to support RF transmission. We have developed various devices such as wearable ubiquitous sensor network (USN) node, wearable chest sensor belt and wrist pulse oximeter for this system. Low power ECG, accelerometer and SpO(2) sensors board was integrated to the wearable USN node for user's health monitoring. The wearable ubiquitous healthcare monitoring system allows physiological data to be transmitted in wireless sensor network using IEEE 802.15.4 from on-body wearable sensor devices to a base-station which is connected to a server PC. Physiological data can be displayed and stored in the server PC continuously.
This study suggests the method for measuring the BP (blood pressure) which it is one among the important vital sign of the human body with a cuffless and non-invasive. The monitoring of the BP is general to use the physical sensor which it directly contacts with the blood vessel in an operation, but this kind of a method can be impossible in the emergency requiring the rapid treatment. In a situation, to take a BP reading, most people use the device which is measured by inflating a cuff around the arm. However, it can measure with these method only the instantaneously BP but the continued BP cannot be shown. This paper shows the wrist type device is developed for the continuous BP measurement which is a non-invasive type. And after analyze and compare to BP data obtained by wearing the wrist type device and reference device, we present the possibility of the cuffless BP.
This paper presents the design and development of a wearable ubiquitous healthcare monitoring system using integrated electrocardiogram (ECG), accelerometer and oxygen saturation (SpO(2)) sensors. In this design, non-intrusive healthcare system was designed based on wireless sensor network (WSN) for wide area coverage with minimum battery power to support RF transmission. We have developed various devices such as wearable ubiquitous sensor network (USN) node, wearable chest sensor belt and wrist pulse oximeter for this system. Low power ECG, accelerometer and SpO(2) sensors board was integrated to the wearable USN node for user's health monitoring. The wearable ubiquitous healthcare monitoring system allows physiological data to be transmitted in wireless sensor network using IEEE 802.15.4 from on-body wearable sensor devices to a base-station which is connected to a server PC. Physiological data can be displayed and stored in the server PC continuously.
Currently, force plates or pressure sensitive insoles are the standard tools to measure ground reaction forces and centre of pressure data during human gait. Force plates, however, impose constraints on foot placement, and the available pressure sensitive insoles measure only one component of force. In this study, shoes instrumented with two force transducers measuring forces and moments in three dimensions were evaluated. Technical performance was assessed by comparing force measurement and centre of pressure reconstructions of the instrumented shoes against a force plate. The effect of the instrumented shoes on gait was investigated using an optical tracking system and a force plate. Instrumented shoes were compared against normal shoes and weighted shoes. The ground reaction force measured with force plate and instrumented shoes differed by 2.2+/-0.1% in magnitude and by 3.4+/-1.3 degrees in direction. The horizontal components differed by 9.9+/-3.8% in magnitude and 26.9+/-10.0 degrees in direction. Centre of pressure location differed by 13.7+/-2.4mm between measurement systems. A MANOVA repeated measures analysis on data of seven subjects, revealed significant differences in gait pattern between shoe types (p</=0.05). A subsequent univariate analysis showed significant differences only in maximum ground reaction force but these could not be attributed to specific shoe types by pair-wise comparison. This study indicates that shoes instrumented with force transducers can be a valuable alternative to current measurement systems if accurate sensing of position and orientation of the force transducers is improved. They are applicable in ambulatory settings and suitable for inverse dynamics analysis.
A truly wearable non-invasive blood pressure (NIBP) sensor--light-weight, compact, unobstrusive, and essentially unnoticeable to the patient--could revolutionize healthcare delivered beyond the traditional walls of medical facilities, offering new ways to care for patients in their everyday surroundings. This paper presents results from our work towards the development of a self-contained, wearable blood pressure sensor. A PPG-based approach to blood pressure monitoring is presented. The design enables significant miniaturization of traditional oscillometric devices without the need for occlusive circumferential pressures. It will be shown how natural raising and lowering of the arm replaces the need for bulky actuators. Additionally, a dual-accelerometer height sensor that is tetherless is proposed and supported by experimental results.
Technologies and techniques that have evolved to aid in the assessment of human locomotion and some of the difficulties associated with the clinical application of quantitative gait analysis are examined. The parameters used for the clinical description of gait are outlined, and examples of application of clinical gait analysis are listed. Problems encountered in measuring and assessing gait patterns are discussed. Some commercially available motion measurement systems are described. Future research directions are suggested.< >
Concomitant advances in communications and medical technology have
led to increasing deployment of telemedicine systems and services around
the world. The goals of these systems are to increase the accessibility
of professional caregivers, increase the quality of care to patients,
and to increase the focus on preventative medicine through early
intervention, all while reducing the overall cost of healthcare. Current
pilot programs are beginning to show that these goals are achievable,
warranting continued investment in telemedicine technologies
Microthermistors are put on the surface of the cerebral cortex to monitor local cerebral blood flow (CBF) continuously with minimal tissue damage and disturbance to the normal physiological state. Using a distributed, dynamic model of the measurement system, the authors simulated the effects of this flow measurement method under isothermal and adiabatic boundary conditions. Numerical results show that the adiabatic boundary condition can provide maximal sensitivity to perfusion changes at physiological perfusion levels. The constant power and constant temperature operating modes are compared in terms of output relation, sensitivity, and frequency response through analytical and numerical solutions. While the steady-state relations between thermistor measurements and perfusion for the two modes do not differ significantly, the constant temperature mode has better frequency response. Analytical results show that the relative sensitivity is the same for the two modes and is approximately proportional to the radius of thermistor.
Three-axis generation and sensing of quasi-static magneticdipole fields provide information sufficient to determine both the position and orientation of the sensor relative to the source. Linear rotation transformations based upon the previous measurements are applied to both the source excitation and sensor output vectors, yielding quantities that are linearly propotional to small changes in the position and orientation. Changes are separated using linear combinations of sensor output vectors, transformed to the desired coordinate frame, and used to update the previous measurements. Practical considerations for a head-tracking application are discussed.
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