Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls
TL;DR: An overview of common ambulatory sensors is presented, followed by a summary of the developments in this field, with an emphasis on the clinical applications of falls detection, falls risk assessment, and energy expenditure.
Abstract: The rapid aging of the world's population, along with an increase in the prevalence of chronic illnesses and obesity, requires adaption and modification of current healthcare models. One such approach involves telehealth applications, many of which are based on sensor technologies for unobtrusive monitoring. Recent technological advances, in particular, involving microelectromechnical systems, have resulted in miniaturized wearable devices that can be used for a range of applications. One of the leading areas for utilization of body-fixed sensors is the monitoring of human movement. An overview of common ambulatory sensors is presented, followed by a summary of the developments in this field, with an emphasis on the clinical applications of falls detection, falls risk assessment, and energy expenditure. The importance of these applications is considerable in light of the global demographic trends and the resultant rise in the occurrence of injurious falls and the decrease of physical activity. The potential of using such monitors in an unsupervised manner for community-dwelling individuals is immense, but entails an array of challenges with regards to design c onsiderations, implementation protocols, and signal analysis processes. Some limitations of the research to date and suggestions for future research are also discussed.
Citations
More filters
TL;DR: The latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges are reviewed.
Abstract: An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.
1,117 citations
TL;DR: A dataset of falls and activities of daily living acquired with a self-developed device composed of two types of accelerometer and one gyroscope is presented, validating findings of other authors and encourages developing new strategies with this new dataset as the benchmark.
Abstract: Research on fall and movement detection with wearable devices has witnessed promising growth However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls These activities were selected based on a survey and a literature analysis We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark
304 citations
TL;DR: Inertial sensors are promising sensors for fall risk assessment and future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables.
Abstract: Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population. Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes. Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%). Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.
301 citations
Cites background from "Sensors-Based Wearable Systems for ..."
...Inertial sensors in physical activity-monitoring systems have been used to detect falls, and several review papers summarize advances in this area [13,22-26]....
[...]
...One of the accelerometer-based physicalactivity-monitoring review papers [26] provided only a brief review of inertial-sensor-based fall risk assessment....
[...]
TL;DR: The existing wearable technologies and the Internet-of-Things concept applied to PD are reviewed and discussed, with an emphasis on how this technological platform may lead to a shift in paradigm in terms of diagnostics and treatment.
Abstract: Current challenges demand a profound restructuration of the global healthcare system. A more efficient system is required to cope with the growing world population and increased life expectancy, which is associated with a marked prevalence of chronic neurological disorders such as Parkinson's disease (PD). One possible approach to meet this demand is a laterally distributed platform such as the Internet of Things (IoT). Real-time motion metrics in PD could be obtained virtually in any scenario by placing lightweight wearable sensors in the patient's clothes and connecting them to a medical database through mobile devices such as cell phones or tablets. Technologies exist to collect huge amounts of patient data not only during regular medical visits but also at home during activities of daily life. These data could be fed into intelligent algorithms to first discriminate relevant threatening conditions, adjust medications based on online obtained physical deficits, and facilitate strategies to modify disease progression. A major impact of this approach lies in its efficiency, by maximizing resources and drastically improving the patient experience. The patient participates actively in disease management via combined objective device- and self-assessment and by sharing information within both medical and peer groups. Here, we review and discuss the existing wearable technologies and the Internet-of-Things concept applied to PD, with an emphasis on how this technological platform may lead to a shift in paradigm in terms of diagnostics and treatment.
257 citations
TL;DR: An automated fall detection approach that requires only a low-cost depth camera and a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM is presented.
Abstract: Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this paper, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques-shape-based fall characterization and a learning-based classifier to distinguish falls from other daily actions. Given a fall video clip, we extract curvature scale space (CSS) features of human silhouettes at each frame and represent the action by a bag of CSS words (BoCSS). Then, we utilize the extreme learning machine (ELM) classifier to identify the BoCSS representation of a fall from those of other actions. In order to eliminate the sensitivity of ELM to its hyperparameters, we present a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM. Using a low-cost Kinect depth camera, we build an action dataset that consists of six types of actions (falling, bending, sitting, squatting, walking, and lying) from ten subjects. Experimenting with the dataset shows that our approach can achieve up to 91.15% sensitivity, 77.14% specificity, and 86.83% accuracy. On a public dataset, our approach performs comparably to state-of-the-art fall detection methods that need multiple cameras.
239 citations
Additional excerpts
...Then, we convolve Γ(u) with a set of Gaussian functions {g(u, σ);σ = 1, 2, . . . , n}: X(u, σ) = x(u) ∗ g(u, σ), Y (u, σ) = y(u) ∗ g(u, σ), to obtain a set of convolved curves {Γσ = (X(u, σ), Y (u, σ))}....
[...]
References
More filters
TL;DR: A practical performance-oriented assessment of mobility is described that incorporates useful features of both approaches and the recommended evaluation centers on the more effective use of readily (and frequently) obtained clinical data.
Abstract: M any people experience a decline in mobility with aging. The multiple chronic diseases and disabilities responsible for this decline also may predispose to falling. This decline is well recognized by clinicians caring for elderly patients. The Canadian Task Force on the Periodic Health Examination not only recognized the problem, but concluded that assessing physical, social, and psychologic functions as they impact on “Progressive Incapacity with Aging” was the most important assessment for patients over age 75.’ Prominent among their list of potentially preventable impairments were locomotory, sensory, and cognitive functions, each of which is intricately related to mobility. The Canadian Task Force further stated that protection of abilities should be emphasized over diagnosis. They believed that establishing the optimal content of the assessment was a high research priority. The purpose of the following discussion is to address the question of content of a functional mobility assessment appropriate for elderly patients. The limitations of relying solely on either a disease-oriented or a gait analytic approach are outlined. A practical performance-oriented assessment of mobility is described that incorporates useful features of both approaches. The recommended evaluation centers on the more effective use of readily (and frequently) obtained clinical data. Although limited to a discussion of ambulation as the expected mode of mobility (necessary because of space limitations), many of the concepts apply to other modes as well (eg, wheelchair).
3,081 citations
"Sensors-Based Wearable Systems for ..." refers background or methods in this paper
...For example, Tinetti’s Performance Oriented Mobility Assessment includes STS transfers, balance control and a 360 turn [32]....
[...]
...The latter included a combination of: Tinetti’s gait and balance assessment [32]; history of falls; and visual, cognitive and mental disorders....
[...]
TL;DR: The mobility test, the best single predictor of recurrent falling, may be useful clinically because it is simple, recreates fall situations, and provides a dynamic, integrated assessment of mobility.
1,507 citations
"Sensors-Based Wearable Systems for ..." refers background in this paper
...For many years, it has been noted that balance and gait maneuvers are most useful in recognizing recurrent fallers [45]....
[...]
01 Jan 2006
TL;DR: Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.
Abstract: The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence
1,334 citations
"Sensors-Based Wearable Systems for ..." refers background in this paper
...3% for correct walking classification [15]....
[...]
...Some systems offer the option of pressing a button to cancel a false alarm, or in the case of a successful recovery [34]; there are also suggestions of using an audio validation tool [15], but these are once again irrelevant if a person is unable to interact with the device....
[...]
...Some algorithms may also employ a threshold for detecting periods of inactivity following a suspected fall, with or without a recovery attempt [15]....
[...]
TL;DR: The purpose of this perspective article is to describe the use of a physiological profile approach to falls risk assessment and prevention that has been developed by the Falls and Balance Research Group of the Prince of Wales Medical Research Institute, Sydney, Australia.
Abstract: The purpose of this perspective article is to describe the use of a physiological profile approach to falls risk assessment and prevention that has been developed by the Falls and Balance Research Group of the Prince of Wales Medical Research Institute, Sydney, Australia. The profile's use for people with a variety of factors that put them at risk for falls is discussed. The Physiological Profile Assessment (PPA) involves a series of simple tests of vision, peripheral sensation, muscle force, reaction time, and postural sway. The tests can be administered quickly, and all equipment needed is portable. The results can be used to differentiate people who are at risk for falls ("fallers") from people who are not at risk for falls ("nonfallers"). A computer program using data from the PPA can be used to assess an individual's performance in relation to a normative database so that deficits can be targeted for intervention. The PPA provides valid and reliable measurements that can be used for assessing falls risk and evaluating the effectiveness of interventions and is suitable for use in a range of physical therapy and health care settings.
1,019 citations
"Sensors-Based Wearable Systems for ..." refers methods in this paper
...With the addition of reaction time testing, a significant correlation of 81% was found between the overall PPA falls risk score and a certain set of extracted time-domain features....
[...]
...The PPA, for example, is a relatively well-validated tool that has been used for thousands of patients globally, but even it claims a falls prediction accuracy of only 75%–80% [50]....
[...]
...These features were also correlated against the PPA subcomponents that include knee extension strength, body sway, vision acuity, and proprioception [52]....
[...]
...Using the Physiological Profile Approach (PPA) [50] for falls risk assessment, the authors were able to define gait parameters on smooth and irregular surfaces, in particular the harmonic ratio, that differed between individuals with varied falls risk....
[...]
...The PPA was also used by Narayanan et al., who utilized a different approach of allowing elderly subjects to perform a directed routine (STS5, AST and TUGT) in a semi-supervised manner while wearing a waist-mounted accelerometer....
[...]
TL;DR: Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity.
Abstract: The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three orthogonally mounted uniaxial piezoresistive accelerometers and can be used to register accelerations covering the amplitude and frequency ranges of human body acceleration. Interinstrument and test-retest experiments showed that the offset and the sensitivity of the TA were equal for each measurement direction and remained constant on two measurement days. Transverse sensitivity was significantly different for each measurement direction, but did not influence accelerometer output (<3% of the sensitivity along the main axis). The data unit enables the on-line processing of accelerometer output to a reliable estimator of physical activity over eight-day periods. Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity (r=0.89). Shortcomings of the system are its low sensitivity to sedentary activities and the inability to register static exercise. The validity of the system for the assessment of normal daily physical activity and specific activities outside the laboratory should be studied in free-living subjects.
951 citations
"Sensors-Based Wearable Systems for ..." refers background in this paper
...integrals of the accelerometry signal magnitudes and the actual EE measures [56]....
[...]