State-of-the-Art Review on Wearable Obstacle Detection Systems Developed for Assistive Technologies and Footwear

Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing foot contact with either the ground or obstacles, leading to a fall. Shoe-mounted sensor systems designed to monitor foot-obstacle interaction are being employed to identify tripping risk and provide corrective feedback. Advances in smart wearable technologies, integrating motion sensors with machine learning algorithms, has led to developments in shoe-mounted obstacle detection. The focus of this review is gait-assisting wearable sensors and hazard detection for pedestrians. This literature represents a research front that is critically important in paving the way towards practical, low-cost, wearable devices that can make walking safer and reduce the increasing financial and human costs of fall injuries

Behavior analysis for aging-in-place using similarity heatmaps

The demand for healthcare services for an increasing population of older adults is faced with the shortage of skilled caregivers and a constant increase in healthcare costs. In addition, the strong preference of the elderly to live independently has been driving much research on "ambient-assisted living" (AAL) systems to support aging-in-place. In this paper, we propose to employ a low-resolution image sensor network for behavior analysis of a home occupant. A network of 10 low-resolution cameras (30x30 pixels) is installed in a service flat of an elderly, based on which the user's mobility tracks are extracted using a maximum likelihood tracker. We propose a novel measure to find similar patterns of behavior between each pair of days from the user's detected positions, based on heatmaps and Earth mover's distance (EMD). Then, we use an exemplar-based approach to identify sleeping, eating, and sitting activities, and walking patterns of the elderly user for two weeks of real-life recordings. The proposed system achieves an overall accuracy of about 94%

A preliminary strategy for fall prevention in the ASBGo smart walker

Fall-related injuries affect a large part of the population and related costs. Thus, there is a concern in studying a fall prevention strategy to minimize the consequences of falls. Walkers are assistive devices used to improve the balance, stability and reduce the load on the lower limb of the user. In this sense, there is a concern to improve the safety in smart walkers and, consequently, to prevent falls in these devices. However, in this field, the only approach is to stop the walker in risk situations. So, the aim of this paper is to define a preliminary strategy to prevent a fall event in the Adaptive System Behaviour Group (ASBGo) Smart Walker. For ASBGo Smart Walker, two modes of security are discussed in this paper. One approach is based on monitoring the center of mass and changing the trajectory when a near fall is detected. The other mechanism consists only in to stop the walker when a dangerous situation is detected. The first or the second mode are activated depending if the user drives the walker with the forearm on forearm support or not.This work has been supported by the FCT – Fundação para a Ciência e Tecnologia - with the scholarship reference PD/BD/141515/2018, by the FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project EML under Grant POCI-01-0247-FEDER-033067, and through the COMPETE 2020 - POCI - with the Reference Project under Grant POCI-01-0145-FEDER-00694

ZIGBEE-BASED SMART FALL DETECTION AND NOTIFICATION SYSTEM WITH WEARABLE SENSOR (e-SAFE)

Fall is one of the serious health issues among elderly population in Malaysia. In the event of a fall, a strong impact may be inflicted on the elderly causing severe injuries or even death. Another research by the National Institutes of Health found that 67% of elderly who fall and fail to seek help within 72 hours are unlikely to survive. Current Personal Emergency Respond System (PERS) often employs the use of a manual emergency button. However this approach may not be useful if the fall victim become unconscious or even not be able to move to reach the emergency button. In addition, such as this approach also requires more time and inadequate to notify and seek for immediate help. This paper attempts to design and implement a smart fall detection system for real time notification known as e-SAFE. This system will automatically detect a fall and notifies the incident instantly to internal and external correspondence. The e-SAFE equipped with a wearable accelerometer sensor, microcontroller, ZigBee transceiver module and Global System for Mobile communications (GSM) device. The in-house correspondence will be notifies though the ZigBee technology, meanwhile the external correspondence will be notified through GSM. Once a fall has been detected by e-SAFE system, a Short Message Service (SMS) and an E-mail will be sent to predefined contacts which is stored in the system. This system will provide a path toward independent living for the elderly while keeping them save

A Wearable Fall Detection System based on LoRa LPWAN Technology

Several technological solutions now available in the market offer the possibility of increasing the independent life of people who by age or pathologies otherwise need assistance. In particular, internet-connected wearable solutions are of considerable interest, as they allow continuous monitoring of the user. However, their use poses different challenges, from the real usability of a device that must still be worn to the performance achievable in terms of radio connectivity and battery life. The acceptability of a technology solution, by a user who would still benefit from its use, is in fact often conditioned by practical problems that impact the person’s normal lifestyle. The technological choices adopted in fact strongly determine the success of the proposed solution, as they may imply limitations both to the person who uses it and to the achievable performance. In this document, targeting the case of a fall detection sensor based on a pair of sensorized shoes, the effectiveness of a real implementation of an Internet of Things technology is examined. It is shown how alarming events, generated in a metropolitan context, are effectively sent to a supervision system through Low Power Wide Area Network technology without the need for a portable gateway. The experimental results demonstrate the effectiveness of the chosen technology, which allows the user to take advantage of the support of a wearable sensor without being forced to substantially change his lifestyle

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials