Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed

Textile-based wearable sensors for assisting sports performance

There is a need for wearable sensors to assess physiological signals and body kinematics during exercise. Such sensors need to be straightforward to use, and ideally the complete system integrated fully within a garment. This would allow wearers to monitor their progress as they undergo an exercise training programme without the need to attach external devices. This takes physiological monitoring into a more natural setting. By developing textile sensors the intelligence is integrated into a sports garment in an innocuous manner. A number of textile based sensors are presented here that have been integrated into garments for various sports applications

Static and dynamic accuracy of an innovative miniaturized wearable platform for short range distance measurements for human movement applications

Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone. To overcome this limitation, we developed a wearable platform specifically designed for human movement analysis applications, which integrates a MIMU and an Infrared Time-of-Flight proximity sensor (IR-ToF), allowing for the estimate of inter-object distance. We proposed a thorough testing protocol for evaluating the IR-ToF sensor performances under experimental conditions resembling those encountered during gait. In particular, we tested the sensor performance for different (i) target colors; (ii) sensor-target distances (up to 200 mm) and (iii) sensor-target angles of incidence (AoI) (up to 60°). Both static and dynamic conditions were analyzed. A pendulum, simulating the oscillation of a human leg, was used to generate highly repeatable oscillations with a maximum angular velocity of 6 rad/s. Results showed that the IR-ToF proximity sensor was not sensitive to variations of both distance and target color (except for black). Conversely, a relationship between error magnitude and AoI values was found. For AoI equal to 0°, the IR-ToF sensor performed equally well both in static and dynamic acquisitions with a distance mean absolute error <1.5 mm. Errors increased up to 3.6 mm (static) and 11.9 mm (dynamic) for AoI equal to ±30°, and up to 7.8 mm (static) and 25.6 mm (dynamic) for AoI equal to ±60°. In addition, the wearable platform was used during a preliminary experiment for the estimation of the inter-foot distance on a single healthy subject while walking. In conclusion, the combination of magneto-inertial unit and IR-ToF technology represents a valuable alternative solution in terms of accuracy, sampling frequency, dimension and power consumption, compared to existing technologies

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

Wearable inertial sensors for human movement analysis

Introduction: The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.Areas covered: Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice.Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine

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