Design and Development of a Multi-sensor Monitoring Device for Arm Rehabilitation

A continuous monitoring process for arm rehabilitation activities are important to provide information of rehabilitation results to be analyzed by therapist. The purpose of monitoring is to help them to improve rehabilitation process. Moreover, a portable and simple home-based rehabilitation device can help patients to improve daily rehabilitation activity. Some previous studies regarding home-based rehabilitation process have shown improvement in promoting human movement recovery. But existing rehabilitation devices are expensive and need to be supervised by physical therapist. Some devices are not so efficient to be used at home due to large size and complex system. In this current work, flex sensor, force sensitive resistors and accelerometer were assessed in order to be implemented as a sensory unit for a portable arm rehabilitation device. Analog signal from the sensors will be conveyed to an Arduino microcontroller for data processing and logging. The results of rehabilitation activity can be used for further monitoring and analysis. Experiments were carried out to determine the feasibility of each sensor towards the design of the new rehabilitation monitoring device. The experiments demonstrate the capabilities of the sensors to produce extended information regarding arm movement activity which can be implemented in the design. A liquid crystal display (LCD) monitor will show to the user the achievement of their exercise activity on daily basis

Uncertainty Investigation for Personalised Lifelogging Physical Activity Intensity Pattern Assessment with Mobile Devices

Lifelogging physical activity (PA) assessment is crucial to healthcare technologies and studies for the purpose of treatments and interventions of chronic diseases. Traditional lifelogging PA monitoring is conducted in non-naturalistic settings by means of wearable devices or mobile phones such as fixed placements, controlled durations or dedicated sensors. Although they achieved satisfactory outcomes for healthcare studies, the practicability become the key issues. Recent advance of mobile devices make lifelogging PA tracking for healthy or unhealthy individuals possible. However, owning to diverse physical characteristics, immaturity of PA recognition techniques, different settings from manufactories and a majority of uncertainties in real life, the results of PA measurement is leading to be inapplicable for PA pattern detection in a long range, especially hardly exploited in the wellbeing monitoring or behaviour changes. This paper investigates and compares uncertainties of existing mobile devices for individual’s PA tracking. Irregular uncertainties (IU) are firstly removed by exploiting Ellipse fitting model, and then monthly density maps that contain regular uncertainties (RU) are constructed based on metabolic equivalents (METs) of different activity types. Five months of four subjects PA intensity changes using the mobile app tracker Moves [1] and Google Fit app on wearable device Samsung wear S2 are carried out from a mobile personalised healthcare platform MHA [2]. The result indicates that uncertainty of PA intensity monitored by mobile phone is 90% lower than wearable device, where the datasets tend to be further explored by healthcare/fitness studies. Whilst PA activity monitoring by mobile phone is still a challenging issue by far due to much more uncertainties than wearable devices

Body sensor network for in-home personal healthcare

A body sensor network solution for personal healthcare under an indoor environment is developed. The system is capable of logging the physiological signals of human beings, tracking the orientations of human body, and monitoring the environmental attributes, which covers all necessary information for the personal healthcare in an indoor environment. The major three chapters of this dissertation contain three subsystems in this work, each corresponding to one subsystem: BioLogger, PAMS and CosNet. Each chapter covers the background and motivation of the subsystem, the related theory, the hardware/software design, and the evaluation of the prototype’s performance

Multimodal Wearable Intelligence for Dementia Care in Healthcare 4.0: A Survey

As a new revolution of Ubiquitous Computing and Internet of Things, multimodal wearable intelligence technique is rapidly becoming a new research topic in both academic and industrial fields. Owning to the rapid spread of wearable and mobile devices, this technique is evolving healthcare from traditional hub-based systems to more personalised healthcare systems. This trend is well-aligned with recent Healthcare 4.0 which is a continuous process of transforming the entire healthcare value chain to be preventive, precise, predictive and personalised, with significant benefits to elder care. But empowering the utility of multimodal wearable intelligence technique for elderly care like people with dementia is significantly challenging considering many issues, such as shortage of cost-effective wearable sensors, heterogeneity of wearable devices connected, high demand for interoperability, etc. Focusing on these challenges, this paper gives a systematic review of advanced multimodal wearable intelligence technologies for dementia care in Healthcare 4.0. One framework is proposed for reviewing the current research of wearable intelligence, and key enabling technologies, major applications, and successful case studies in dementia care, and finally points out future research trends and challenges in Healthcare 4.0

Management, processing and dissemination of sensory data for the Earth Resource Technology Satellite

Data center for management, processing, and dissemination of photographic products generated by ERT

Smart textiles for improved quality of life and cognitive assessment

Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). This concept paper presents a smart textile prototype to both entertain and monitor/assess the behavior of the relevant clients. The prototype includes physical computing components for music playing and simple interaction, but additionally games and data logging systems, to determine baselines of activity and interaction. Using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors woven into a fabric, the study demonstrates the kinds of augmentations possible over the normal manipulation of the traditional non-smart activity apron by incorporating light and sound effects as feedback when patients interact with different regions of the textile. A data logging system will record the patient’s behavioral patterns. This would include the location, frequency, and time of the patient’s activities within the different textile areas. The textile will be placed across the laps of the resident, which they then play with, permitting the development of a behavioral profile through the gamification of cognitive tests. This concept paper outlines the development of a prototype sensor system and highlights the challenges related to its use in a care home setting. The research implements a wide range of functionality through a novel architecture involving loosely coupling and concentrating artifacts on the top layer and technology on the bottom layer. Components in a loosely coupled system can be replaced with alternative implementations that provide the same services, and so this gives the solution the best flexibility. The literature shows that existing architectures that are strongly coupled result in difficulties modeling different individuals without incurring significant costs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Multiple Density Maps Information Fusion for Effectively Assessing Intensity Pattern of Lifelogging Physical Activity

Physical activity (PA) measurement is a crucial task in healthcare technology aimed at monitoring the progression and treatment of many chronic diseases. Traditional lifelogging PA measures require relatively high cost and can only be conducted in controlled or semi-controlled environments, though they exhibit remarkable precision of PA monitoring outcomes. Recent advancement of commercial wearable devices and smartphones for recording one’s lifelogging PA has popularized data capture in uncontrolled environments. However, due to diverse life patterns and heterogeneity of connected devices as well as the PA recognition accuracy, lifelogging PA data measured by wearable devices and mobile phones contains much uncertainty thereby limiting their adoption for healthcare studies. To improve the feasibility of PA tracking datasets from commercial wearable/mobile devices, this paper proposes a lifelogging PA intensity pattern decision making approach for lifelong PA measures. The method is to firstly remove some irregular uncertainties (IU) via an Ellipse fitting model, and then construct a series of monthly based hour-day density map images for representing PA intensity patterns with regular uncertainties (RU) on each month. Finally it explores Dempster-Shafer theory of evidence fusing information from these density map images for generating a decision making model of a final personal lifelogging PA intensity pattern. The approach has significantly reduced the uncertainties and incompleteness of datasets from third party devices. Two case studies on a mobile personalized healthcare platform MHA [1] connecting the mobile app Moves are carried out. The results indicate that the proposed approach can improve effectiveness of PA tracking devices or apps for various types of people who frequently use them as a healthcare indicator