Labeling of Activity Recognition Datasets: Detection of Misbehaving Users

Automatic recognition of user’s activities by means of wearable devices is a key element of many e-health applications, ranging from rehabilitation to monitoring of elderly citizens. Activity recognition methods generally rely on the availability of annotated training sets, where the traces collected using sensors are labelled with the real activity carried out by the user. We propose a method useful to automatically identify misbehaving users, i.e. the users that introduce inaccuracies during the labeling phase. The method is semi-supervised and detects misbehaving users as anomalies with respect to accurate ones. Experimental results show that misbehaving users can be detected with more than 99% accuracy

A wearable hybrid IEEE 802.15.4-2011 ultra-wideband/inertial sensor platform for ambulatory tracking

Ultra-Wideband (UWB) transceivers and low-cost micro electro mechanical systems (MEMS) based inertial sensors are proving a promising hybrid combination for location specific wearable applications. While several hybrid systems have been proposed to date, current approaches consider inertial sensors and UWB as ad-hoc components working in isolation. As a result issues surrounding extensive infrastructure requirements, synchronization, and limitations associated with the mutual sharing of inertial data have arisen. In an attempt to address such limitations, this paper presents a fully-coupled architecture whereby standardised IEEE 802.15.4-2011 UWB is employed for both ranging and as a mechanism for exchanging inertial data between the nodes of a network. A proof-of-concept system is implemented and tested for a single ambulatory use case scenario. Basic fusion algorithms are employed and the preliminary results show the benefits of a fully-coupled approach when compared with traditional standalone inertial navigation

Gait Analysis for Early Neurodegenerative Diseases Classification through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation

A Wearable Indoor Navigation System for Blind and Visually Impaired Individuals

Indoor positioning and navigation for blind and visually impaired individuals has become an active field of research. The development of a reliable positioning and navigational system will reduce the suffering of the people with visual disabilities, help them live more independently, and promote their employment opportunities. In this work, a coarse-to-fine multi-resolution model is proposed for indoor navigation in hallway environments based on the use of a wearable computer called the eButton. This self-constructed device contains multiple sensors which are used for indoor positioning and localization in three layers of resolution: a global positioning system (GPS) layer for building identification; a Wi-Fi - barometer layer for rough position localization; and a digital camera - motion sensor layer for precise localization. In this multi-resolution model, a new theoretical framework is developed which uses the change of atmospheric pressure to determine the floor number in a multistory building. The digital camera and motion sensors within the eButton acquire both pictorial and motion data as a person with a normal vision walks along a hallway to establish a database. Precise indoor positioning and localization information is provided to the visually impaired individual based on a Kalman filter fusion algorithm and an automatic matching algorithm between the acquired images and those in the pre-established database. Motion calculation is based on the data from motion sensors is used to refine the localization result. Experiments were conducted to evaluate the performance of the algorithms. Our results show that the new device and algorithms can precisely determine the floor level and indoor location along hallways in multistory buildings, providing a powerful and unobtrusive navigational tool for blind and visually impaired individuals

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Providing Proximity Safety and Speeding Alerts to Workers on Construction Sites Using Bluetooth Low Energy RTLS

The construction sector is one of the most dangerous industrial sectors. Struck-by object or equipment is one of the main causes of fatal accidents on construction sites. Although many regulations have been designed for struck-by accidents, these accidents are still causing many injuries and fatalities. According to the U.S. Bureau of Labor Statistic, the struck-by accidents has led to 112 deaths on construction site in 2018. The application of real-time location systems (RTLS) on construction sites provides new possibilities in construction safety management. Previous researchers have proposed using RTLS to track the location of workers and equipment on construction sites to improve construction safety. However, the previous methods have some limitations (e.g. cabling problems, positioning quality). Furthermore, providing effective safety alerts to workers within dangerous proximity to equipment has not been addressed in previous research. This research aims to develop a method for providing near real-time proximity alerts to workers on construction sites using Bluetooth Low Energy (BLE) RTLS based on angle of arrival (AOA). This RTLS can provide acceptable accuracy coupled with large coverage without the need of timing cables. Also, with the support of two-way communications between the tags and sensors, it is possible to provide vibro-tactile alerts to the workers using wristbands. In addition, alerts representing different cases of proximities and speeding were defined. The prototype system has the following features: (1) less cabling by using wireless technologies for data transmission, (2) less false alerts by generating the alerts to specific entities based on the micro-schedule of activities, (3) easily perceived alerts. Tests were conducted on a construction site of an electric substation to test the accuracy of the RTLS and the performance of the prototype system. The test results indicated that the prototype system is capable of detecting proximities and generating timely alerts to the involved entities

Design and Application of Wireless Body Sensors

Hörmann T. Design and Application of Wireless Body Sensors. Bielefeld: Universität Bielefeld; 2019

Telemedicine and its application in telemedicine management

Telemedicine can be defined as the extensive depiction of providing medical and healthcare services by using telecommunications structures. Information Technology (IT) which covers controlling, interactive media, pattern recognition, knowledge management, image and signal processing: have empowered an extensive array of telemedicine applications to be supported. The joined consequence of the expansion of the global population and maturing populace in most advanced countries offersascent to an increasing interest on the public health system. The effect on public health systems in various nations were further empowered by a change in the lifestyle and environmental contamination which further increases the demand for health systems. This is obvious from the pattern of perpetual ailments and complication arising from obesity-related conditions which attack youthful individuals over the previous decade. Currently, the financial prosperity which blesses the present generation is a result of the diligent work done by our fore fathers and the rapacious exploitation of the natural resources that will eventually cause various issues to the upcoming generation. Therefore, we should seize the responsibility of caring for the elderly who tirelessly sacrificed their time for the betterment of the current generation. Nevertheless, we are attempting to upgrade medicinal technology to enhance our well-being, and to furnish a supportable healthcare system for the upcoming era. Telemedicine is poised as a means of fulfilling our obligations to the adolescents and the elderly