A review of wearable sensors and systems with application in rehabilitation

An ambulatory method of identifying anterior cruciate ligament reconstructed gait patterns

The use of inertial sensors to characterize pathological gait has traditionally been based on the calculation of temporal and spatial gait variables from inertial sensor data. This approach has proved successful in the identification of gait deviations in populations where substantial differences from normal gait patterns exist; such as in Parkinsonian gait. However, it is not currently clear if this approach could identify more subtle gait deviations, such as those associated with musculoskeletal injury. This study investigates whether additional analysis of inertial sensor data, based on quantification of gyroscope features of interest, would provide further discriminant capability in this regard. The tested cohort consisted of a group of anterior cruciate ligament reconstructed (ACL-R) females and a group of non-injured female controls, each performed ten walking trials. Gait performance was measured simultaneously using inertial sensors and an optoelectronic marker based system. The ACL-R group displayed kinematic and kinetic deviations from the control group, but no temporal or spatial deviations. This study demonstrates that quantification of gyroscope features can successfully identify changes associated with ACL-R gait, which was not possible using spatial or temporal variables. This finding may also have a role in other clinical applications where small gait deviations exist.

Designing auditory cues for Parkinson's disease gait rehabilitation

Recent works have proved that Parkinson's disease (PD) patients can be largely benefit by performing rehabilitation exercises based on audio cueing and music therapy. Specially, gait can benefit from repetitive sessions of exercises using auditory cues. Nevertheless, all the experiments are based on the use of a metronome as auditory stimuli. Within this work, Human-Computer Interaction methodologies have been used to design new cues that could benefit the long-term engagement of PD patients in these repetitive routines. The study has been also extended to commercial music and musical pieces by analyzing features and characteristics that could benefit the engagement of PD patients to rehabilitation tasks.

Clinical overview of the need for technologies for around-the-clock monitoring of the health status of severely disabled autistic children

Despite the growing incidence and costs of autism, little has been done to apply technology advancements to the challenges faced by autistic individuals. The recent introduction of digital data collection to track the progress of treatment interventions has been an important contribution, but there are many other opportunities for technology to facilitate the development of autistic children and to assist the clinical staff who work with them. The realization of wearable devices to identify and track behaviors would significantly reduce the manual input of data into digital devices, and wireless physiological monitoring has the potential to provide predictors of unwanted behaviors. The development of such tools would change the intervention paradigms that currently exist.

Does external walking environment affect gait patterns?

The objective of this work is to develop an understanding of the relationship between mobility metrics obtained outside of the clinic or laboratory and the context of the external environment. Ten subjects walked with an inertial sensor on each shank and a wearable camera around their neck. They were taken on a thirty minute walk in which they mobilized over the following conditions; normal path, busy hallway, rough ground, blind folded and on a hill. Stride time, stride time variability, stance time and peak shank rotation rate during swing were calculated using previously published algorithms. Stride time was significantly different between several of the conditions. Technological advances mean that gait variables can now be captured as patients go about their daily lives. The results of this study show that the external environment has a significant impact on the quality of gait metrics. Thus, context of external walking environment is an important consideration when analyzing ambulatory gait metrics from the unsupervised home and community setting.

Hand contour detection in wearable camera video using an adaptive histogram region of interest

BACKGROUND

Monitoring hand function at home is needed to better evaluate the effectiveness of rehabilitation interventions. Our objective is to develop wearable computer vision systems for hand function monitoring. The specific aim of this study is to develop an algorithm that can identify hand contours in video from a wearable camera that records the user's point of view, without the need for markers.

METHODS

The two-step image processing approach for each frame consists of: (1) Detecting a hand in the image, and choosing one seed point that lies within the hand. This step is based on a priori models of skin colour. (2) Identifying the contour of the region containing the seed point. This is accomplished by adaptively determining, for each frame, the region within a colour histogram that corresponds to hand colours, and backprojecting the image using the reduced histogram.

RESULTS

In four test videos relevant to activities of daily living, the hand detector classification accuracy was 88.3%. The contour detection results were compared to manually traced contours in 97 test frames, and the median F-score was 0.86.

CONCLUSION

This algorithm will form the basis for a wearable computer-vision system that can monitor and log the interactions of the hand with its environment.

Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

A Survey on Ambient Intelligence in Health Care

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds

Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

Flexible sensors based on nanoparticles

Flexible sensors can be envisioned as promising components for smart sensing applications, including consumer electronics, robotics, prosthetics, health care, safety equipment, environmental monitoring, homeland security and space flight. The current review presents a concise, although admittedly nonexhaustive, didactic review of some of the main concepts and approaches related to the use of nanoparticles (NPs) in flexible sensors. The review attempts to pull together different views and terminologies used in the NP-based sensors, mainly those established via electrical transduction approaches, including, but, not confined to: (i) strain-gauges, (ii) flexible multiparametric sensors, and (iii) sensors that are unaffected by mechanical deformation. For each category, the review presents and discusses the common fabrication approaches and state-of-the-art results. The advantages, weak points, and possible routes for future research, highlighting the challenges for NP-based flexible sensors, are presented and discussed as well.

M-BRIDGE: Wireless portable onbody aggregator and visualizer system for Wireless Body Sensor Network

Advances made in electronics, intelligent and wireless technologies enable individuals to self-observe their health states anywhere anytime. The shift in self care becomes a promising paradigm to alleviate burdens on centralized institutional care. As a result, Wireless Body Sensor Network (WBSN) personal health solutions can be seen increasingly although medical community still has concerns on their usability and applicability. Especially, there is still lacking in portable wireless wearable gateway to integrate WBSN into existing healthcare solutions. To fulfill this gap, we design and develop MobilE on-Body aGgregator and vIsualizer Device (M-BRIDGE) system using Android smart phone. Our proposed solution fully supports the needs of flexible device interfacing, data aggregation, efficient data distribution and user-friendly visualization. We also explain how M-BRIDGE's unique features and operation can complement with and fulfill the deficiency of existing WBSN healthcare solutions. We finally present the details of implementation and technical evaluation as well as discussion on the potential issues and future works.

Exergaming for balance training of elderly: state of the art and future developments

Fall injuries are responsible for physical dysfunction, significant disability, and loss of independence among elderly. Poor postural control is one of the major risk factors for falling but can be trained in fall prevention programs. These however suffer from low therapy adherence, particularly if prevention is the goal. To provide a fun and motivating training environment for elderly, exercise games, or exergames, have been studied as balance training tools in the past years. The present paper reviews the effects of exergame training programs on postural control of elderly reported so far. Additionally we aim to provide an in-depth discussion of technologies and outcome measures utilized in exergame studies. Thirteen papers were included in the analysis. Most of the reviewed studies reported positive results with respect to improvements in balance ability after a training period, yet few reached significant levels. Outcome measures for quantification of postural control are under continuous dispute and no gold standard is present. Clinical measures used in the studies reviewed are well validated yet only give a global indication of balance ability. Instrumented measures were unable to detect small changes in balance ability as they are mainly based on calculating summary statistics, thereby ignoring the time-varying structure of the signals. Both methods only allow for measuring balance after the exergame intervention program. Current developments in sensor technology allow for accurate registration of movements and rapid analysis of signals. We propose to quantify the time-varying structure of postural control during gameplay using low-cost sensor systems. Continuous monitoring of balance ability leaves the user unaware of the measurements and allows for generating user-specific exergame training programs and feedback, both during one game and in timeframes of weeks or months. This approach is unique and unlocks the as of yet untapped potential of exergames as balance training tools for community dwelling elderly.

A Passive Radio-Frequency Identification (RFID) Gas Sensor With Self-Correction Against Fluctuations of Ambient Temperature

Uncontrolled fluctuations of ambient temperature in the field typically greatly reduce accuracy of gas sensors. In this study, we developed an approach for the self-correction against fluctuations of ambient temperature of individual gas and vapor sensors. The main innovation of our work is in the temperature correction which is accomplished without the need for a separate uncoated reference sensor or a separate temperature sensor. Our sensors are resonant inductor-capacitor-resistor (LCR) transducers coated with sensing materials and operated as multivariable passive (battery-free) radio-frequency identification (RFID) sensors. Using our developed approach, we performed quantitation of an exemplary vapor over the temperature range from 25 to 40 °C. This technical solution will be attractive in numerous applications where temperature stabilization of a gas sensor or addition of auxiliary temperature or uncoated reference sensors is prohibitive.

Implementing technology-based embedded assessment in the home and community life of individuals aging with disabilities: a participatory research and development study

PURPOSE

The goal of the study was to investigate the accuracy, feasibility and acceptability of implementing an embedded assessment system in the homes of individuals aging with disabilities.

METHOD

We developed and studied a location tracking system, UbiTrack, which can be used for both indoor and outdoor location sensing. The system was deployed in the homes of five participants with spinal cord injuries, muscular dystrophy, multiple sclerosis and late effects of polio. We collected sensor data throughout the deployment, conducted pre and post interviews and collected weekly diaries to measure ground truth.

RESULTS

The system was deployed successfully although there were challenges related to system installation and calibration. System accuracy ranged from 62% to 87% depending upon room configuration and number of wireless access points installed. In general, participants reported that the system was easy to use, did not require significant effort on their part and did not interfere with their daily lives.

CONCLUSIONS

Embedded assessment has great potential as a mechanism to gather ongoing information about the health of individuals aging with disabilities; however, there are significant challenges to its implementation in real-world settings with people with disabilities that will need to be resolved before it can be practically implemented.

IMPLICATIONS FOR REHABILITATION

Technology-based embedded assessment has the potential to promote health for adults with disabilities and allow for aging in place. It may also reduce the difficulty, cost and intrusiveness of health measurement. Many new commercial and non-commercial products are available to support embedded assessment; however, most products have not been well-tested in real-world environments with individuals aging with disability. Community settings and diverse population of people with disabilities pose significant challenges to the implementation of embedded assessment systems.

Design of a wearable sensing system for human motion monitoring in physical rehabilitation

Human motion monitoring and analysis can be an essential part of a wide spectrum of applications, including physical rehabilitation among other potential areas of interest. Creating non-invasive systems for monitoring patients while performing rehabilitation exercises, to provide them with an objective feedback, is one of the current challenges. In this paper we present a wearable multi-sensor system for human motion monitoring, which has been developed for use in rehabilitation. It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device. The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking, as required, for example, in activity recognition, physical/athletic performance evaluation and rehabilitation.

The digital patient

Despite efforts, the detection of patients who are deteriorating in hospital is often later than it should be. Several technologies could provide the basis of a solution. Recording of vital signs could be improved by both automated transmission of the measured parameters to an electronic patient record and the use of unobtrusive wearable monitors that track the patient's physiology continuously. Electronic charting systems could make the recorded vital signs readily available for further processing. Software algorithms could identify such patients with greater sensitivity and specificity than the existing, paper-based track-and-trigger systems. Electronic storage of vital signs also makes intelligent alerting and remote patient surveillance possible. However, the potential of these technologies depends strongly on implementation, with poor-quality deployment likely to worsen patient care.

WIRELESS ACCELEROMETER SYSTEM FOR QUANTIFYING DISPARITY OF HEMIPLEGIC GAIT USING THE FREQUENCY DOMAIN

The proper allocation of a therapy strategy and dosage is fundamentally associated with the quantified evaluation of gait quality. Wireless accelerometer systems for the evaluation of quantified hemiplegic gait characteristics has been successfully applied in inherently autonomous environments through the consideration of the temporal domain of the gait acceleration waveform. The frequency domain has notable potential for identifying the quantified disparity of the affected leg and unaffected leg through the application of a tandem-activated wireless accelerometer system mounted to the lateral malleolus of each lower leg through an elastic band. The quantification of disparity for hemiplegic gait via the application of wireless accelerometers was applied in an outdoor environment, while walking on a sidewalk. In addition, the wireless accelerometers were tandem activated while the subject had achieved steady-state gait status, which mitigated the need to subjectively remove starting acceleration and stopping deceleration aspects of the gait cycle. Four predominant frequencies within the 0–5 Hz bandwidth demonstrated a considerable degree of accuracy and reliability. The organization of the four predominant frequencies for both affected leg and unaffected leg were found to be disparate in a statistically significant manner, implicating a disparity of the rhythmicity respective of the affected leg in contrast to the unaffected leg in hemiplegic gait. These preliminary findings may advance gait quantification techniques, which may improve the efficacy of gait rehabilitation therapy. Enclosed are the initial test and evaluation of a tandem-activated wireless accelerometer system using the frequency domain for ascertaining a quantified disparity of hemiplegic gait.

Metrics for assessing the reliability of a telemedicine remote monitoring system

OBJECTIVE

The goal of this study was to assess using new metrics the reliability of a real-time health monitoring system in homes of older adults.

MATERIALS AND METHODS

The "MobileCare Monitor" system was installed into the homes of nine older adults >75 years of age for a 2-week period. The system consisted of a wireless wristwatch-based monitoring system containing sensors for location, temperature, and impacts and a "panic" button that was connected through a mesh network to third-party wireless devices (blood pressure cuff, pulse oximeter, weight scale, and a survey-administering device). To assess system reliability, daily phone calls instructed participants to conduct system tests and reminded them to fill out surveys and daily diaries. Phone reports and participant diary entries were checked against data received at a secure server.

RESULTS

Reliability metrics assessed overall system reliability, data concurrence, study effectiveness, and system usability. Except for the pulse oximeter, system reliability metrics varied between 73% and 92%. Data concurrence for proximal and distal readings exceeded 88%. System usability following the pulse oximeter firmware update varied between 82% and 97%. An estimate of watch-wearing adherence within the home was quite high, about 80%, although given the inability to assess watch-wearing when a participant left the house, adherence likely exceeded the 10 h/day requested time. In total, 3,436 of 3,906 potential measurements were obtained, indicating a study effectiveness of 88%.

CONCLUSIONS

The system was quite effective in providing accurate remote health data. The different system reliability measures identify important error sources in remote monitoring systems.

Sensors: views of staff of a disability service organization

Sensors have become ubiquitous in their reach and scope of application. They are a technological cornerstone for various modes of health surveillance and participatory medicine-such as quantifying oneself; they are also employed to track people with certain as impairments perceived ability differences. This paper presents quantitative and qualitative data of an exploratory, non-generalizable study into the perceptions, attitudes and concerns of staff of a disability service organization, that mostly serve people with intellectual disabilities, towards the use of various types of sensor technologies that might be used by and with their clients. In addition, perspectives of various types of privacy issues linked to sensors, as well data regarding the concept of quantified self were obtained. Our results highlight the need to involve disabled people and their support networks in sensor and quantified-self discourses, in order to prevent undue disadvantages.

Evaluation of a textile-based wearable system for the electrocardiogram monitoring in cardiac patients

AIMS

We developed a textile-based wearable system, named MagIC, for the unobtrusive monitoring of one electrocardiogram (ECG) lead, respiratory frequency and motion. In the present study, we investigated the ability of this system to monitor cardiac rhythm and arrhythmic events in cardiac patients.

METHODS AND RESULTS

The study was carried out by comparing ECG tracings simultaneously recorded by MagIC and traditional ECG devices (Trad-ECG) in 40 cardiac patients at rest and during physical exercise. Data were manually scored by two cardiologists. At rest the artefact rates observed with MagIC and Trad-ECG were virtually identical (1.4% of the registered signal); while during physical exercise the artefact rate observed with MagIC was much lower than with Trad-ECG (4.07 vs. 17.31%). Recordings from MagIC allowed a correct identification of the type of rhythm in the vast majority of patients (92.5%) and an estimation of PQ interval and QRS duration similar to Trad-ECG (<0.016 s). MagIC displayed a good performance in detecting arrhythmias, with only 14 misclassified events out of 3618, and both specificity and sensitivity being above 99%. No practical difference was observed in the estimation of the beat-by-beat RR interval by the two methods.

CONCLUSION

This study indicates that in static condition MagIC has a capability of monitoring cardiac rhythm and arrhythmic events which is comparable with what obtainable by a traditional one-lead ECG recorder. During movement MagIC provides an ECG signal of better quality.

A review on architectures and communications technologies for wearable health-monitoring systems

Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in “LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments”, published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications.