Development of a mobile technology system to measure shoulder range of motion

In patients with shoulder movement impairment, assessing and monitoring shoulder range of motion is important for determining the severity of impairments due to disease or injury and evaluating the effects of interventions. Current clinical methods of goniometry and visual estimation require an experienced user and suffer from low inter-rater reliability. More sophisticated techniques such as optical or electromagnetic motion capture exist but are expensive and restricted to a specialised laboratory environment.;Inertial measurement units (IMU), such as those within smartphones and smartwatches, show promise as tools bridge the gap between laboratory and clinical techniques and accurately measure shoulder range of motion during both clinic assessments and in daily life.;This study aims to develop an Android mobile application for both a smartphone and a smartwatch to assess shoulder range of motion. Initial performance characterisation of the inertial sensing capabilities of both a smartwatch and smartphone running the application was conducted against an industrial inclinometer, free-swinging pendulum and custom-built servo-powered gimbal.;An initial validation study comparing the smartwatch application with a universal goniometer for shoulder ROM assessment was conducted with twenty healthy participants. An impaired condition was simulated by applying kinesiology tape across the participants shoulder girdle. Agreement, intra and inter-day reliability were assessed in both the healthy and impaired states.;Both the phone and watch performed with acceptable accuracy and repeatability during static (within ±1.1°) and dynamic conditions where it was strongly correlated to the pendulum and gimbal data (ICC > 0.9). Both devices could perform accurately within optimal responsiveness range of angular velocities compliant with humerus movement during activities of daily living (frequency response of 377°/s and 358°/s for the phone and watch respectively).;The concurrent agreement between the watch and the goniometer was high in both healthy and impaired states (ICC > 0.8) and between measurement days (ICC > 0.8). The mean absolute difference between the watch and the goniometer were within the accepted minimal clinically important difference for shoulder movement (5.11° to 10.58°).;The results show promise for the use of the developed Android application to be used as a goniometry tool for assessment of shoulder ROM. However, the limits of agreement across all the tests fell out with the acceptable margin and further investigation is required to determine validity. Evaluation of validity in clinical impairment patients is also required to assess the feasibility of the use of the application in clinical practice.In patients with shoulder movement impairment, assessing and monitoring shoulder range of motion is important for determining the severity of impairments due to disease or injury and evaluating the effects of interventions. Current clinical methods of goniometry and visual estimation require an experienced user and suffer from low inter-rater reliability. More sophisticated techniques such as optical or electromagnetic motion capture exist but are expensive and restricted to a specialised laboratory environment.;Inertial measurement units (IMU), such as those within smartphones and smartwatches, show promise as tools bridge the gap between laboratory and clinical techniques and accurately measure shoulder range of motion during both clinic assessments and in daily life.;This study aims to develop an Android mobile application for both a smartphone and a smartwatch to assess shoulder range of motion. Initial performance characterisation of the inertial sensing capabilities of both a smartwatch and smartphone running the application was conducted against an industrial inclinometer, free-swinging pendulum and custom-built servo-powered gimbal.;An initial validation study comparing the smartwatch application with a universal goniometer for shoulder ROM assessment was conducted with twenty healthy participants. An impaired condition was simulated by applying kinesiology tape across the participants shoulder girdle. Agreement, intra and inter-day reliability were assessed in both the healthy and impaired states.;Both the phone and watch performed with acceptable accuracy and repeatability during static (within ±1.1°) and dynamic conditions where it was strongly correlated to the pendulum and gimbal data (ICC > 0.9). Both devices could perform accurately within optimal responsiveness range of angular velocities compliant with humerus movement during activities of daily living (frequency response of 377°/s and 358°/s for the phone and watch respectively).;The concurrent agreement between the watch and the goniometer was high in both healthy and impaired states (ICC > 0.8) and between measurement days (ICC > 0.8). The mean absolute difference between the watch and the goniometer were within the accepted minimal clinically important difference for shoulder movement (5.11° to 10.58°).;The results show promise for the use of the developed Android application to be used as a goniometry tool for assessment of shoulder ROM. However, the limits of agreement across all the tests fell out with the acceptable margin and further investigation is required to determine validity. Evaluation of validity in clinical impairment patients is also required to assess the feasibility of the use of the application in clinical practice

EldyIoT: IoT assistive system for elderly

The Internet of Things (IoT) is one of the most promising technologies for the near future. IoT has penetrated many industries such as smart cities, smart homes, smart cars, healthcare, agriculture and manufacturing. In the health sector, the number of use cases has increased as a consequence of the improvement in the quality and effectiveness of the provision of hospital and care services. This work presents a solution to reduce the distance between caregivers and patients in a non-intrusive way, allowing information about the health status and mobility of patients to be available and accessible anywhere, anytime. Consecutively, it will increase the confidence of caregivers in relation to the health status of monitored patients and, on the other hand, it will increase the confidence of patients that their health status is being monitored frequently. Thus, this thesis presents a framework for physical monitoring of elderly people based on smartwatch devices. As an integral part of the solution, we present a cross-platform mobile application and a software framework assembled to support the processes of collecting, processing, storing, displaying and analyzing data.A Internet das Coisas (IoT) é uma das tecnologias mais promissoras para o futuro próximo. A IoT penetrou em muitos setores, como cidades inteligentes, casas inteligentes, carros inteligentes, saúde, agricultura e manufatura. No sector da saúde, o número de casos de uso, tem aumentado como consequência do incremento na qualidade e eficácia na prestação de serviços hospitalares e de cuidados. Este trabalho, apresenta uma solução para reduzir a distância entre cuidadores e pacientes de forma não intrusiva, permitindo que informações sobre o estado de saúde e mobilidade dos pacientes estejam disponíveis e acessíveis em qualquer lugar, e a qualquer hora. Consecutivamente, aumentará a confiança dos cuidadores em relação ao estado de saúde dos pacientes acompanhados e, por outro lado, aumentará a confiança dos pacientes de que o seu estado de saúde está a ser monitorizado com frequência. Assim, esta tese apresenta um framework para monitorização física de idosos baseada em dispositivos smartwatch. Como parte integrante da solução, apresentamos uma aplicação móvel cross-platform e uma estrutura de software montada para dar suporte aos processos de recolher, processamento, armazenamento, exibição e análise de dados

The business opportunities of implementing wearable based products in the health and life insurance industries

The ability to collect biometric data continuously was recently enabled by the development and massification of wearable technologies - computers that are incorporated into items of clothing and accessories which can be worn on the body - unlocking huge opportunities for health and life insurers. Although a great deal of research has been done regarding the technical aspects of these devices, very few works explore the business and managerial implications of implementing wearables into insurance products. Through the combination of secondary data and in-depth expert interviews, this work analyses the impact of wearables and wearable data in the insurance value chain, identifying the main opportunities and challenges to leverage such a technology. This research concludes that in spite of the current narrow use of wearable devices as engagement tools in insurance wellness programs designed to drive user loyalty, this technology has the potential to accelerate the underwriting process, support preventive care, expand the customer base, enable dynamic pricing and enhance the customer experience as part of a connected health ecosystem. Customer adoption, data privacy and legislation are some of the main obstacles for insurers to leverage this technology, on top of the necessary IT infrastructure and data management capabilities which insurers are acquiring mainly through partnerships with innovative players. By implementing wearables technologies, health and life insurers may benefit from reductions in operational costs, new revenue streams and ultimately gains in competitive advantage.A recolha contínua de dados biométricos foi recentemente possibilitada pelo desenvolvimento e massificação dos wearables – computadores incorporados na roupa e acessórios que podem ser vestidos – que representam uma grande oportunidade para as seguradoras de vida e saúde. Apesar de já existir bastante pesquisa sobre os aspetos técnicos destes dispositivos, o mesmo não se verifica ao nível do negócio e da gestão, na implementação de wearables na indústria seguradora. Através da combinação de dados secundários e entrevistas a experts da indústria, este trabalho analisa o impacto dos wearables na cadeia de valor das seguradoras, identificando as principais oportunidades e desafios da sua implementação. A pesquisa conclui que apesar da atual aplicação dos wearables ser limitada, visto que são utilizados como pontos de contacto com o consumidor em programas de bem-estar promovidos pelas seguradoras para reforçar a lealdade à marca, esta tecnologia tem o potencial para acelerar a subscrição de seguros, promover ações de cuidado preventivo de saúde, expandir a base de clientes, possibilitar a prática de preços dinâmicos e melhorar a experiência do consumidor integrados num ecossistema conectado de saúde. A adoção, a privacidade de dados e a legislação são alguns dos principais obstáculos aquando da implementação destes dispositivos, a par da infraestrutura de TI e capacidades de gestão de dados que as seguradoras têm adquirido maioritariamente via parcerias com novas empresas. A implementação dos wearables pode assim contribuir para a redução de custos operacionais, criação de novas fontes de receita e ganhos de vantagem competitiva para as seguradoras

Wearables in medicine

Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches

Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review

Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and nonmotor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods, and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment

Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica Sinic

A data fusion-based hybrid sensory system for older people’s daily activity recognition.

Population aged 60 and over is growing faster. Ageing-caused changes, such as physical or cognitive decline, could affect people’s quality of life, resulting in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) has become one of the most promising assistive technologies for older people’s daily life. Literature in HAR suggests that each sensor modality has its strengths and limitations and single sensor modalities may not cope with complex situations in practice. This research aims to design and implement a hybrid sensory HAR system to provide more comprehensive, practical and accurate surveillance for older people to assist them living independently. This reseach: 1) designs and develops a hybrid HAR system which provides a spatio- temporal surveillance system for older people by combining the wrist-worn sensors and the room-mounted ambient sensors (passive infrared); the wearable data are used to recognize the defined specific daily activities, and the ambient information is used to infer the occupant’s room-level daily routine; 2): proposes a unique and effective data fusion method to hybridize the two-source sensory data, in which the captured room-level location information from the ambient sensors is also utilized to trigger the sub classification models pretrained by room-assigned wearable data; 3): implements augmented features which are extracted from the attitude angles of the wearable device and explores the contribution of the new features to HAR; 4:) proposes a feature selection (FS) method in the view of kernel canonical correlation analysis (KCCA) to maximize the relevance between the feature candidate and the target class labels and simultaneously minimizes the joint redundancy between the already selected features and the feature candidate, named mRMJR-KCCA; 5:) demonstrates all the proposed methods above with the ground-truth data collected from recruited participants in home settings. The proposed system has three function modes: 1) the pure wearable sensing mode (the whole classification model) which can identify all the defined specific daily activities together and function alone when the ambient sensing fails; 2) the pure ambient sensing mode which can deliver the occupant’s room-level daily routine without wearable sensing; and 3) the data fusion mode (room-based sub classification mode) which provides a more comprehensive and accurate surveillance HAR when both the wearable sensing and ambient sensing function properly. The research also applies the mutual information (MI)-based FS methods for feature selection, Support Vector Machine (SVM) and Random Forest (RF) for classification. The experimental results demonstrate that the proposed hybrid sensory system improves the recognition accuracy to 98.96% after applying data fusion using Random Forest (RF) classification and mRMJR-KCCA feature selection. Furthermore, the improved results are achieved with a much smaller number of features compared with the scenario of recognizing all the defined activities using wearable data alone. The research work conducted in the thesis is unique, which is not directly compared with others since there are few other similar existing works in terms of the proposed data fusion method and the introduced new feature set

Smart Sensing Technologies for Personalised Coaching

People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and can age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people toward healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support but also gives us an incentive to set goals and to do more. This book presents some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes

Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States