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

A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation

Lower extremity amputees suffer from mobility limitations which will result in a degradation of their quality of life. Wearable sensors are frequently used to assess spatio-temporal, kinematic and kinetic parameters providing the means to establish an interactive control of the amputee-prosthesis-environment system. Gait events and the gait phase detection of an amputee’s locomotion are vital for controlling lower limb prosthetic devices. The paper presents an approach to real-time gait event detection for lower limb amputees using a wireless gyroscope attached to the shank when performing level ground and ramp activities. The results were validated using both healthy and amputee subjects and showed that the time differences in identifying Initial Contact (IC) and Toe Off (TO) events were larger in a transfemoral amputee when compared to the control subjects and a transtibial amputee (TTA). Overall, the time difference latency lies within a range of ± 50 ms while the detection rate was 100% for all activities. Based on the validated results, the IC and TO events can be accurately detected using the proposed system in both control subjects and amputees when performing activities of daily living and can also be utilized in the clinical setup for rehabilitation and assessing the performance of lower limb prosthesis users

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

Real-time gait event detection using a miniature gyroscope to improve rehabilitation for artificial lower limb users

This paper presents a simple heuristic rule based on real-time gait event detection algorithm for transfemoral amputees based on gyroscope signal and validate with footswitches for ramp activities with different inclinatio

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio

Recognition of false alarms in fall detection systems

Falls are a major cause of hospitalization and injury-related deaths among the elderly population. The detrimental effects of falls, as well as the negative impact on health services costs, have led to a great interest on fall detection systems by the health-care industry. The most promising approaches are those based on a wearable device that monitors the movements of the patient, recognizes a fall and triggers an alarm. Unfortunately such techniques suffer from the problem of false alarms: some activities of daily living are erroneously reported as falls, thus reducing the confidence of the user. This paper presents a novel approach for improving the detection accuracy which is based on the idea of identifying specific movement patterns into the acceleration data. Using a single accelerometer, our system can recognize these patterns and use them to distinguish activities of daily living from real falls; thus the number of false alarms is reduced

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

Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction

Falls in older adults are a major clinical problem often resulting in serious injury. The costly nature of clinic-based testing for the propensity of falling and a move towards homebased care and monitoring of older adults has led to research in wearable sensing technologies for identifying fall-related parameters from activities of daily living. This paper discusses the development of two algorithms for identifying periods of walking (gait events) and extracting characteristic patterns for each gait event (gait features) with a view to identifying the propensity to fall in older adults. In this paper, we present an evaluation of the algorithms involving a small real-world dataset collected from healthy adults in an uncontrolled environment. 92.5% of gait events were extracted from lower leg gyroscope data from 5 healthy adults (total duration of 33 hours) and over 95% of the gait characteristic points were identified in this data. A user interface to aid clinicians review gait features from walking events captured over multiple days is also proposed. The work presents initial steps in the development of a platform for monitoring patients within their daily routine in uncontrolled environments to inform clinical decision-making related to falls