Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach

To expand the potential use of in-shoe motion sensors (IMSs) in daily healthcare or activity monitoring applications for healthy subjects, we propose a real-time temporal estimation method for gait parameters concerning bilateral lower limbs (GPBLLs) that uses a single IMS and is based on a gait event detection approach. To validate the established methods, data from 26 participants recorded by an IMS and a reference 3D motion analysis system were compared. The agreement between the proposed method and the reference system was evaluated by the intraclass correlation coefficient (ICC). The results showed that, by averaging over five continuous effective strides, all time parameters achieved precisions of no more than 30 ms and agreement at the “excellent” level, and the symmetry indexes of the stride time and stance phase time achieved precisions of 1.0% and 3.0%, respectively, and agreement at the “good” level. These results suggest our method is effective and shows promise for wide use in many daily healthcare or activity monitoring applications for healthy subjects.

A Comprehensive Review on Wearable Health Monitoring Systems

This paper presents a comprehensive review of the wearable healthcare monitoring systems proposed by the researchers to date. One of the earliest wearable recorders, named “a silicon locket for ECG monitoring”, was developed at the Indian Institute of Technology, Bombay, in 2003. Thus, the wearable health monitoring systems, started with the acquisition of a single signal/ parameter to the present generation smart and affordable multi-parameter recording/monitoring systems, have evolved manifolds in these two decades. Wearable systems have dramatically changed in terms of size, cost, functionality, and accuracy. The early-day wearable recorders were with limited functionalities against today’s systems, e.g., Apple’s iWatch which comprises abundant health monitoring features like heart rate monitoring, breathing app, accelerometers, smart walking/ activity monitoring, and alerts. Most of the present-day smartphones are not only capable of recording various health features like body temperature, heart rate, photoplethysmograph (PPG) signal, calory consumption, smart activity monitoring, stress measurement, etc. through different apps, but they also help the user to get monitored by a family physician via GSM or even internet of things (IoT). One of the latest, state-of-the-art real-time personal health monitoring systems, Wearable IoT-cloud-based health monitoring system (WISE), is a beautiful amalgamation of body area sensor network (BASN) and IoT framework for ubiquitous health monitoring. The future of wearable health monitoring systems will be far beyond the IoT and BASN.

Asparaginase activity monitoring and management of asparaginase hypersensitivity reactions in Canada

Introduction Pegaspargase can cause anti-asparaginase antibody formation, which can decrease its effectiveness without causing any clinically apparent reaction (silent inactivation). When a patient has silent inactivation, a switch to Erwinia anti-asparaginase is warranted, but there is currently a global shortage of Erwinia. The only way to identify silent inactivation is to measure an asparaginase level. However, routine asparaginase level monitoring is not currently standard of care at all Canadian centers. This study aims to identify variations in practice regarding asparaginase level monitoring and Erwinia use. Methods A 21-item survey was developed using OPINIO software and distributed to all Pediatric Hematology–Oncologists in Canada from February to October 2020. Results Respondents represented 15 hospitals across each region of Canada (response rate = 52%). Only 39.2% of respondents reported routinely measuring asparaginase levels, yet 53% of respondents have modified therapy from pegaspargase to Erwinia in up to half of their patients. The most common reason for not measuring asparaginase levels was not knowing how to use levels clinically (25.5%). There was variation in the timing of levels and their target. Conclusions We identified substantial variation in asparaginase activity monitoring practices across Canada. Therefore, future research should aim to develop a national practice guideline on asparaginase activity monitoring.

IoT-assisted physical education training network virtualization and resource management using a deep reinforcement learning system

The Internet of Things (IoT) development made it possible for technology to communicate physical education by connecting cost-effective heterogeneous devices and digital applications to uncontrolled and accessible environments. The traditional physical education monitoring environment creates crucial manual efforts on athletes' activity observations and tracking consistently. Similarly, remote monitoring and assessment of athletes in sports training seem to be barriers to physical education monitoring and training. It creates various chances to improve training and education through technology advancements like IoT and deep learning. Students can efficiently monitor their physical behavior to increase their physical and psychological benefits. The IoT-assisted physical activity monitoring device is proposed to track students' physical activity and enhance outcomes. The management ability allows students to organize and increase speed their physical activity in a wellness manner. In addition, this study examines the connections between monitoring ability which is an essential component for sports activities and physical activity. This system collects essential information from IoT-based wearable devices that interact with the data in real time by virtualizing the device. The IoT network includes several device activities and monitors the heartbeat and physical body temperature of a person. The analysis of specific studies and student feedback shows that the designed virtual system of physical educations is effective in its application and implementation and provides a reliable guide for developing student physical educational systems. The experimental analysis is evaluated; the solution offered is developing and supporting physical education and training approaches in reality and creates healthy environment systems to solve the health monitoring challenges posed by IoT devices. The proposed method has achieved extraordinary physical activity monitoring compared to the conventional systems, as shown by experimental findings. The simulation analysis of physical education can help students and improve the associated aspects of physical abilities with high accuracy ratio (98.3), prediction ratio (96.5%), interaction ratio (94.4%), performance ratio (95.1%), the efficiency ratio (93.2), F-score (92.2%), and reduce error rate (17.5%) and physical activity patterns.

The Effects of Remote Activity Monitoring on Family Caregivers of People Living With Dementia Over an 18-Month Period

Technology interventions for older persons and long-term care are generally utilized as real-time data capture tools to complement clinical or family care for older persons or as interventions themselves designed to improve important dementia care outcomes. Although research on novel technological interventions for people with Alzheimer's disease and related dementias (ADRD) and their family caregivers has grown considerably in the past two decades, much of this work continues to focus on design, feasibility, and acceptability (with a need for conceptual refinement in these areas) and less on controlled outcome studies. The objective of this experimental mixed methods demonstration was to determine the 18-month effectiveness of remote activity monitoring (RAM) technology in improving outcomes among family caregivers of community-dwelling persons with dementia. We used an embedded experimental mixed methods design, collecting qualitative data within the structure of a traditional randomized controlled trial ([QUAN+qual]→QUAN) over an 18-month period for 171 dementia caregivers. Change in caregiver self-efficacy, sense of competence, and caregiver distress served as the main quantitative outcomes of interest. Individual growth curve models indicated that the RAM technology did not have direct effects on caregiving outcomes, and although the qualitative findings indicated several potential moderators of RAM effectiveness on caregiving outcomes, the inclusion of these qualitatively-identified moderators did not result in statistically significant (p < .05) effects. Ensuring effective human care management alongside RAM technology may help to overcome the barriers reported by dementia caregivers in this demonstration study.

Feasibility and Acceptability of the Technology-Based Fall Risk Assessments for Older Adults

Promising technologies, which are simple, portable, quick, non-invasive, and inexpensive, may open new horizons on fall risk assessments and provide important information for older adults. We used a mixed-methods approach to examine the feasibility and acceptability of technology-based fall risk assessments, including the BTrackS Balance System, Bioelectrical Impedance Analysis, and activity monitoring devices among older adults. Data were collected via a Qualtrics survey and interviews. The acceptability was measured by the Senior Technology Acceptance (STA) and semi-structured interviews with 15 participants. The STA consists of four domains with 14 items, and the semi-structured interview includes three main questions related to experiences about balance performance tests, body composition, and activity monitoring. One hundred twenty-four community-dwelling older adults completed the online survey, and 15 older adults participated in the interviews. The majority of participants were female, and 72% had no history of falls. Race and ethnicity were 17% Hispanic, 7% African Americans, and 3% Asian Americans. About 7% had COVID-19 positive, 31% reported fear of COVID, and 14.5% were afraid of losing their life to COVID. The word-of-mouth strategy and key person approach were used and had an incredible impact on the recruitment process. None of the participants had ever had their fall risk and fear of falling assessed before agreeing to participate in this study. The technology-based fall risk assessments were feasible and acceptable. About 78% of participants liked the idea of using technology to assess falls risk, and 79% agreed that using technology would enhance their effectiveness in daily activities.