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

Wearable inertial sensors and range of motion metrics in physical therapy remote support

Abstract. The practice of physiotherapy diagnoses patient ailments which are often treated by the daily repetition of prescribed physiotherapeutic exercise. The effectiveness of the exercise regime is dependent on regular daily repetition of the regime and the correct execution of the prescribed exercises. Patients often have issues learning unfamiliar exercises and performing the exercise with good technique. This design science research study examines a back squat classifier design to appraise patient exercise regime away from the physiotherapy practice. The scope of the exercise appraisal is limited to one exercise, the back squat. Kinematic data captured with commercial inertial sensors is presented to a small group of physiotherapists to illustrate the potential of the technology to measure range of motion (ROM) for back squat appraisal. Opinions are considered from two fields of physiotherapy, general musculoskeletal and post-operative rehabilitation. While the exercise classifier is considered not suitable for post-operative rehabilitation, the opinions expressed for use in general musculoskeletal physiotherapy are positive. Kinematic data captured with gyroscope sensors in the sagittal plane is analysed with Matlab to develop a method for back squat exercise recognition and appraisal. The artefact, a back squat classifier with appraisal features is constructed from Matlab scripts which are proven to be effective with kinematic data from a novice athlete

Low-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors

In this paper, we present a novel multi-sensor fusion method to build a human skeleton. We propose to fuse the joint po- sition information obtained from the popular Kinect sensor with more precise estimation of body segment orientations provided by a small number of wearable inertial sensors. The use of inertial sensors can help to address many of the well known limitations of the Kinect sensor. The precise calcu- lation of joint angles potentially allows the quantification of movement errors in technique training, thus facilitating the use of the low-cost Kinect sensor for accurate biomechani- cal purposes e.g. the improved human skeleton could be used in visual feedback-guided motor learning, for example. We compare our system to the gold standard Vicon optical mo- tion capture system, proving that the fused skeleton achieves a very high level of accuracy

Promoting a healthy ageing workforce: use of Inertial Measurement Units to monitor potentially harmful trunk posture under actual working conditions

Musculoskeletal disorders, particularly those involving the low back, represent a major health concern for workers, and originate significant consequences for the socio-economic system. As the average age of the population is gradually (yet steadily) increasing, such phenomenon directly reflects on labor market raising the need to create the optimal conditions for jobs which must be sustainable for the entire working life of an individual, while constantly ensuring good health and quality of life. In this context, prevention and management of low back disorders (LBDs) should be effective starting from the working environment. To this purpose, quantitative, reliable and accurate tools are needed to assess the main parameters associated to the biomechanical risk. In the last decade, the technology of wearable devices has made available several options that have been proven suitable to monitor the physical engagement of individuals while they perform manual or office working tasks. In particular, the use of miniaturized Inertial Measurement Units (IMUs) which has been already tested for ergonomic applications with encouraging results, could strongly facilitate the data collection process, being less time- and resources-consuming with respect to direct or video observations of the working tasks. Based on these considerations, this research intends to propose a simplified measurement setup based on the use of a single IMUs to assess trunk flexion exposure, during actual shifts, in occupations characterized by significant biomechanical risk. Here, it will be demonstrated that such approach is feasible to monitor large groups of workers at the same time and for a representative duration which can be extended, in principle, to the entire work shift without perceivable discomfort for the worker or alterations of the performed task. Obtained data, which is easy to interpret, can be effectively employed to provide feedback to workers thus improving their working techniques from the point of view of safety. They can also be useful to ergonomists or production engineers regarding potential risks associated with specific tasks, thus supporting decisions or allowing a better planning of actions needed to improve the interaction of the individual with the working environment

Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments

Comparison of wearable measurement systems for estimating trunk postures in manual material handling, A

2017 Fall.Includes bibliographical references.Epidemiologic studies have established that awkward trunk postures during manual materials handling are associated with an increased risk of developing occupational low back disorders. With recent advances in motion capture technology, emerging wearable measurement systems have been designed to quantify trunk postures for exposure assessments. Wearable measurement systems integrate portable microelectromechanical sensors, real-time processing algorithms, and large memory capacity to effectively quantify trunk postures. Wearable measurement systems have been available primarily as research tools, but are now quickly becoming accessible to health and safety professionals for industrial application. Although some of these systems can be highly complex and deter health and safety professionals from using them, other systems can serve as a simpler, more user-friendly alternative. These simple wearable measurement systems are designed to be less intricate, allowing health and safety professionals to be more willing to utilize them in occupational posture assessments. Unfortunately, concerns regarding the comparability and agreement between simple and complex wearable measurement systems for estimating trunk postures are yet to be fully addressed. Furthermore, application of wearable measurement systems has been affected by the lack of adaptability of sensor placement to work around obstructive equipment and bulky gear workers often wear on the job. The aims of the present study were to 1) compare the Bioharness™3, a simple wearable measurement system, to Xsens™, a complex wearable measurement system, for estimating trunk postures during simulated manual material handling tasks and 2) to explore the effects of Xsens sensor placement on assessing trunk postures. Thirty participants wore the two systems simultaneously during simulated tasks in the laboratory that involved reaching, lifting, lowering, and pushing a load for ten minutes. Results indicated that the Bioharness 3 and Xsens systems are comparable for strictly estimating trunk postures that involved flexion and extension of 30° or less. Although limited to a short range of trunk postures, the Bioharness also exhibited moderate to strong agreement and correlations with the Xsens system for measuring key metrics commonly used in exposure assessments, including amplitude probability distribution functions and percent time spent in specific trunk posture categories or bins. The Bioharness is suggested to be an a more intuitive alternative to the Xsens system for posture analysis, but industrial use of the device should be warranted in the context of the exposure assessment goals. In addition, a single motion sensor from the Xsens system placed on the sternum yielded comparable and consistent estimates to a sensor secured on the sternum relative to a motion sensor on the sacrum. Estimates included descriptive measures of trunk flexion and extension and percent time spent in specific trunk posture categories. Using one motion sensor instead of two may serve as an alternative for sensor placement configuration in situations where worker portable equipment or personal preference prevents preferred sensor placement

Wearable Sensor Gait Analysis for Fall Detection Using Deep Learning Methods

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide range of gait variations in older. Choosing the appropriate sensor and placing it in the most suitable location are essential components of a robust real-time fall detection system. This dissertation implements various detection models to analyze and mitigate injuries due to falls in the senior community. It presents different methods for detecting falls in real-time using deep learning networks. Several sliding window segmentation techniques are developed and compared in the first study. As a next step, various methods are implemented and applied to prevent sampling imbalances caused by the real-world collection of fall data. A study is also conducted to determine whether accelerometers and gyroscopes can distinguish between falls and near-falls. According to the literature survey, machine learning algorithms produce varying degrees of accuracy when applied to various datasets. The algorithm’s performance depends on several factors, including the type and location of the sensors, the fall pattern, the dataset’s characteristics, and the methods used for preprocessing and sliding window segmentation. Other challenges associated with fall detection include the need for centralized datasets for comparing the results of different algorithms. This dissertation compares the performance of varying fall detection methods using deep learning algorithms across multiple data sets. Furthermore, deep learning has been explored in the second application of the ECG-based virtual pathology stethoscope detection system. A novel real-time virtual pathology stethoscope (VPS) detection method has been developed. Several deep-learning methods are evaluated for classifying the location of the stethoscope by taking advantage of subtle differences in the ECG signals. This study would significantly extend the simulation capabilities of standard patients by allowing medical students and trainees to perform realistic cardiac auscultation and hear cardiac auscultation in a clinical environment

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc