A new application of smart walker for quantitative analysis of human walking

International audienceThis paper presents a new nonintrusive device for everyday gait analysis and health monitoring. The system is a standard rollator equipped with encoders and inertial sensors. The assisted walking of 25 healthy elderly and 23 young adults are compared to develop walking quality index. The subjects were asked to walk on a straight trajectory and an L-shaped trajectory respectively. The walking trajectory, which is missing in other gait analysis methods, is calculated based on the encoder data. The obtained trajectory and steps are compared with the results of a motion capture system. The gait analysis results show that new index obtained by using the walker measurements, and not available otherwise, are very discriminating, e.g., the elderly have larger lateral motion and maneuver area, smaller angular velocity during turning, their walking accuracy is lower and turning ability is weaker although they have almost the same walking velocity as the young people

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

Salford postgraduate annual research conference (SPARC) 2012 proceedings

These proceedings bring together a selection of papers from the 2012 Salford Postgraduate Annual Research Conference (SPARC). They reflect the breadth and diversity of research interests showcased at the conference, at which over 130 researchers from Salford, the North West and other UK universities presented their work. 21 papers are collated here from the humanities, arts, social sciences, health, engineering, environment and life sciences, built environment and business

Identifying the time profile of everyday activities in the home using smart meter data

Activities are a descriptive term for the common ways households spend their time. Examples include cooking, doing laundry, or socialising. Smart meter data can be used to generate time profiles of activities that are meaningful to households’ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates a multi-step methodology for inferring hourly time profiles of ten household activities using smart meter data, supplemented by individual appliance plug monitors and environmental sensors. First, household interviews, video ethnography, and technology surveys are used to identify appliances and devices in the home, and their roles in specific activities. Second, ‘ontologies’ are developed to map out the relationships between activities and technologies in the home. One or more technologies may indicate the occurrence of certain activities. Third, data from smart meters, plug monitors and sensor data are collected. Smart meter data measuring aggregate electricity use are disaggregated and processed together with the plug monitor and sensor data to identify when and for how long different activities are occurring. Sensor data are particularly useful for activities that are not always associated with an energy-using device. Fourth, the ontologies are applied to the disaggregated data to make inferences on hourly time profiles of ten everyday activities. These include washing, doing laundry, watching TV (reliably inferred), and cleaning, socialising, working (inferred with uncertainties). Fifth, activity time diaries and structured interviews are used to validate both the ontologies and the inferred activity time profiles. Two case study homes are used to illustrate the methodology using data collected as part of a UK trial of smart home technologies. The methodology is demonstrated to produce reliable time profiles of a range of domestic activities that are meaningful to households. The methodology also emphasises the value of integrating coded interview and video ethnography data into both the development of the activity inference process

An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring

New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility to support prevention, diagnostic work-up, therapeutic decisions, and individual disease monitoring with a continuous assessment of gait and mobility. This overview article provides the technological as well as medical aspects of smart shoes within this rising area of digital health applications, and is designed especially for the novel reader in this specific field. It also stresses the need for closer interdisciplinary interactions between technological and medical experts to bridge the gap between research and practice. Smart shoes can be envisioned to serve as pervasive wearable computing systems that enable innovative solutions and services for the promotion of healthy living and the transformation of health care

A deep learning solution for real-time human motion decoding in smart walkers

Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Eletrónica Médica)The treatment of gait impairments has increasingly relied on rehabilitation therapies which benefit from the use of smart walkers. These walkers still lack advanced and seamless Human-Robot Interaction, which intuitively understands the intentions of human motion, empowering the user’s recovery state and autonomy, while reducing the physician’s effort. This dissertation proposes the development of a deep learning solution to tackle the human motion decoding problematic in smart walkers, using only lower body vision information from a camera stream, mounted on the WALKit Smart Walker, a smart walker prototype for rehabilitation purposes. Different deep learning frameworks were designed for early human motion recognition and detec tion. A custom acquisition method, including a smart walker’s automatic driving algorithm and labelling procedure, was also designed to enable further training and evaluation of the proposed frameworks. Facing a 4-class (stop, walk, turn right/left) classification problem, a deep learning convolutional model with an attention mechanism achieved the best results: an offline f1-score of 99.61%, an online calibrated instantaneous precision higher than 97% and a human-centred focus slightly higher than 30%. Promising results were attained for early human motion detection, with enhancements in the focus of the proposed architectures. However, further improvements are still needed to achieve a more reliable solution for integration in a smart walker’s control strategy, based in the human motion intentions.O tratamento de distúrbios da marcha tem apostado cada vez mais em terapias de reabilitação que beneficiam do uso de andarilhos inteligentes. Estes ainda carecem de uma Interação Humano-Robô avançada e eficaz, capaz de entender, intuitivamente, as intenções do movimento humano, fortalecendo a recuperação autónoma do paciente e reduzindo o esforço médico. Esta dissertação propõe o desenvolvimento de uma solução de aprendizagem para o problema de descodificação de movimento humano em andarilhos inteligentes, usando apenas vídeos recolhidos pelo WALKit Smart Walker, um protótipo de andarilho inteligente usado para reabilitação. Foram desenvolvidos algoritmos de aprendizagem para o reconhecimento e detecção precoces de movimento humano. Um método de aquisição personalizado, incluindo um algoritmo de condução e labelização automatizados, foi projetado para permitir o conseguinte treino e avaliação dos algoritmos propostos. Perante a classificação de 4 ações (parar, andar, virar à direita/esquerda), um modelo convolucional com um mecanismo de atenção alcançou os melhores resultados: f1-score offline de 99,61%, precisão instantânea calibrada online de superior a 97 % e um foco centrado no ser humano ligeiramente superior a 30%. Com esta dissertação alcançaram-se resultados promissores para a detecção precoce de movimento humano, com aprimoramentos no foco dos algoritmos propostos. No entanto, ainda são necessárias melhorias adicionais para alcançar uma solução mais robusta para a integração na estratégia de controlo de um andarilho inteligente, com base nas intenções de movimento do utilizador