A Wearable Ground Reaction Force Sensor System and Its Application to the Measurement of Extrinsic Gait Variability

Wearable sensors for gait analysis are attracting wide interest. In this paper, a wearable ground reaction force (GRF) sensor system and its application to measure extrinsic gait variability are presented. To validate the GRF and centre of pressure (CoP) measurements of the sensor system and examine the effectiveness of the proposed method for gait analysis, we conducted an experimental study on seven volunteer subjects. Based on the assessment of the influence of the sensor system on natural gait, we found that no significant differences were found for almost all measured gait parameters (p-values < 0.05). As for measurement accuracy, the root mean square (RMS) differences for the two transverse components and the vertical component of the GRF were 7.2% ± 0.8% and 9.0% ± 1% of the maximum of each transverse component and 1.5% ± 0.9% of the maximum vertical component of GRF, respectively. The RMS distance between both CoP measurements was 1.4% ± 0.2% of the length of the shoe. The area of CoP distribution on the foot-plate and the average coefficient of variation of the triaxial GRF, are the introduced parameters for analysing extrinsic gait variability. Based on a statistical analysis of the results of the tests with subjects wearing the sensor system, we found that the proposed parameters changed according to walking speed and turning (p-values < 0.05)

Gait Analysis Using Wearable Sensors

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

A New Proxy Measurement Algorithm with Application to the Estimation of Vertical Ground Reaction Forces Using Wearable Sensors

Measurement of the ground reaction forces (GRF) during walking is typically limited to laboratory settings, and only short observations using wearable pressure insoles have been reported so far. In this study, a new proxy measurement method is proposed to estimate the vertical component of the GRF (vGRF) from wearable accelerometer signals. The accelerations are used as the proxy variable. An orthogonal forward regression algorithm (OFR) is employed to identify the dynamic relationships between the proxy variables and the measured vGRF using pressure-sensing insoles. The obtained model, which represents the connection between the proxy variable and the vGRF, is then used to predict the latter. The results have been validated using pressure insoles data collected from nine healthy individuals under two outdoor walking tasks in non-laboratory settings. The results show that the vGRFs can be reconstructed with high accuracy (with an average prediction error of less than 5.0%) using only one wearable sensor mounted at the waist (L5, fifth lumbar vertebra). Proxy measures with different sensor positions are also discussed. Results show that the waist acceleration-based proxy measurement is more stable with less inter-task and inter-subject variability than the proxy measures based on forehead level accelerations. The proposed proxy measure provides a promising low-cost method for monitoring ground reaction forces in real-life settings and introduces a novel generic approach for replacing the direct determination of difficult to measure variables in many applications

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Validity and reliability of a wearable insole pressure system for measuring gait parameters to identify safety hazards in construction

Purpose Construction workers are frequently exposed to safety hazards on sites. Wearable sensing systems (e.g. wearable inertial measurement units (WIMUs), wearable insole pressure system (WIPS)) have been used to collect workers' gait patterns for distinguishing safety hazards. However, the performance of measuring WIPS-based gait parameters for identifying safety hazards as compared to a reference system (i.e. WIMUs) has not been studied. Therefore, this study examined the validity and reliability of measuring WIPS-based gait parameters as compared to WIMU-based gait parameters for distinguishing safety hazards in construction. Design/methodology/approach Five fall-risk events were conducted in a laboratory setting, and the performance of the proposed approach was assessed by calculating the mean difference (MD), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and intraclass correlation coefficient (ICC) of five gait parameters. Findings Comparable results of MD, MAE, MAPE and RMSE were found between WIPS-based gait parameters and the reference system. Furthermore, all measured gait parameters had validity (ICC = 0.751) and test-retest reliability (ICC = 0.910) closer to 1, indicating a good performance of measuring WIPS-based gait parameters for distinguishing safety hazards. Research limitations/implications Overall, this study supports the relevance of developing a WIPS as a noninvasive wearable sensing system for identifying safety hazards on construction sites, thus highlighting the usefulness of its applications for construction safety research. Originality/value This is the first study to examine the performance of a wearable insole pressure system for identifying safety hazards in construction

Machine Learning-based Detection of Compensatory Balance Responses and Environmental Fall Risks Using Wearable Sensors

Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide, with serious and costly consequences. Compensatory balance responses (CBRs) are reactions to recover stability following a loss of balance, potentially resulting in a fall if sufficient recovery mechanisms are not activated. While performance of CBRs are demonstrated risk factors for falls in seniors, the frequency, type, and underlying cause of these incidents occurring in everyday life have not been well investigated. This study was spawned from the lack of research on development of fall risk assessment methods that can be used for continuous and long-term mobility monitoring of the geri- atric population, during activities of daily living, and in their dwellings. Wearable sensor systems (WSS) offer a promising approach for continuous real-time detection of gait and balance behavior to assess the risk of falling during activities of daily living. To detect CBRs, we record movement signals (e.g. acceleration) and activity patterns of four muscles involving in maintaining balance using wearable inertial measurement units (IMUs) and surface electromyography (sEMG) sensors. To develop more robust detection methods, we investigate machine learning approaches (e.g., support vector machines, neural networks) and successfully detect lateral CBRs, during normal gait with accuracies of 92.4% and 98.1% using sEMG and IMU signals, respectively. Moreover, to detect environmental fall-related hazards that are associated with CBRs, and affect balance control behavior of seniors, we employ an egocentric mobile vision system mounted on participants chest. Two algorithms (e.g. Gabor Barcodes and Convolutional Neural Networks) are developed. Our vision-based method detects 17 different classes of environmental risk factors (e.g., stairs, ramps, curbs) with 88.5% accuracy. To the best of the authors knowledge, this study is the first to develop and evaluate an automated vision-based method for fall hazard detection

Body sensor networks: smart monitoring solutions after reconstructive surgery

Advances in reconstructive surgery are providing treatment options in the face of major trauma and cancer. Body Sensor Networks (BSN) have the potential to offer smart solutions to a range of clinical challenges. The aim of this thesis was to review the current state of the art devices, then develop and apply bespoke technologies developed by the Hamlyn Centre BSN engineering team supported by the EPSRC ESPRIT programme to deliver post-operative monitoring options for patients undergoing reconstructive surgery. A wireless optical sensor was developed to provide a continuous monitoring solution for free tissue transplants (free flaps). By recording backscattered light from 2 different source wavelengths, we were able to estimate the oxygenation of the superficial microvasculature. In a custom-made upper limb pressure cuff model, forearm deoxygenation measured by our sensor and gold standard equipment showed strong correlations, with incremental reductions in response to increased cuff inflation durations. Such a device might allow early detection of flap failure, optimising the likelihood of flap salvage. An ear-worn activity recognition sensor was utilised to provide a platform capable of facilitating objective assessment of functional mobility. This work evolved from an initial feasibility study in a knee replacement cohort, to a larger clinical trial designed to establish a novel mobility score in patients recovering from open tibial fractures (OTF). The Hamlyn Mobility Score (HMS) assesses mobility over 3 activities of daily living: walking, stair climbing, and standing from a chair. Sensor-derived parameters including variation in both temporal and force aspects of gait were validated to measure differences in performance in line with fracture severity, which also matched questionnaire-based assessments. Monitoring the OTF cohort over 12 months with the HMS allowed functional recovery to be profiled in great detail. Further, a novel finding of continued improvements in walking quality after a plateau in walking quantity was demonstrated objectively. The methods described in this thesis provide an opportunity to revamp the recovery paradigm through continuous, objective patient monitoring along with self-directed, personalised rehabilitation strategies, which has the potential to improve both the quality and cost-effectiveness of reconstructive surgery services.Open Acces

Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis

Human fatigue due to repetitive and physically challenging jobs may result in poor performance and a Work-related Musculoskeletal Disorder (WMSD). Thus, the importance of being able to monitor fatigue to implement preventative interventions cannot be overstated. This study was designed to monitor fatigue through the development of a methodology that objectively classifies an individual’s level of fatigue in the workplace by utilizing the motion sensors embedded in smartphones. An experiment consisting of squatting tasks, primarily involving the lower extremity musculature, was conducted with 24 participants using a smartphone attached to their right shank. Using Borg’s Ratings of Perceived Exertion (RPE) to label gait data, we developed machine learning algorithms to classify each individual’s gait into two (no- vs. strong-fatigue), three (no-, medium-, and strong-fatigue) and four (no-, low-, medium-, and strong-fatigue) levels of fatigue, for which accuracy of 91%, 76%, and 61% were obtained, respectively. The outcomes of this study may facilitate the implementation of a proactive approach supporting the continuous monitoring of a worker’s fatigue level, which may subsequently enhance workers’ performance and reduce the risk of WMSDs