Tracking system based on accelerometry for users with restricted physical activity

This article aims to develop a minimally intrusive system of care and monitoring. Furthermore, the goal is to get a cheap, comfortable and, especially, efficient system which controls the physical activity carried out by the user. All this, is based on the data of accelerometry analysis which are obtained by a mobile phone. Besides this, we will develop a comprehensive system for consulting the activity obtained in order to provide families and care staff an interface through which to observe the condition of the individual subject to monitoring.Ministerio de Ciencia e Innovación ARTEMISA TIN2009-14378-C02-01Ministerio de Ciencia e Innovación FAMENET TSI2006-13390-C02-02Junta de Andalucía CUBICO TIC214

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings

The role of mobile technology for fall risk assessment for individuals with multiple sclerosis

Multiple Sclerosis (MS) is a chronic, progressive neurogenerative disease that affects one million people in the United States (Wallin et al., 2019). Common MS symptoms include impaired coordination, poor walking and balance, and fatigue, and these symptoms put people with MS (pwMS) at a higher risk for falls (Cameron & Nilsagard, 2018). Falls are highly prevalent among pwMS and can result in detrimental consequences including bone fractures and even death (Matsuda et al., 2011). To prevent falls and fall related injuries, it is important to first assess for multiple risk factors and then intervene through targeted treatments (Palumbo et al., 2015). Fall risk can be assessed through self-report measures, clinical performance tests, or with technology such as force plates and motion capture systems (Kanekar & Aruin, 2013). However, clinicians have time constraints, technology is expensive, and trained personnel is needed. Moreover, due to the COVID-19 pandemic, access to in-person clinical visits is limited. As a result, pwMS may not receive fall risk screening and remain vulnerable to fall related injuries. Mobile technology offers a solution to increase access to fall risk screening using an affordable, ubiquitous, and portable tool (Guise et al., 2014; Marrie et al., 2019). Therefore, the overarching goal of this study was to develop a usable fall risk health application (app) for pwMS to self-assess their fall risk in the home setting. Four studies were performed: 1) smartphone accelerometry was tested to measure postural control in pwMS; 2) a fall risk algorithm was developed for a mobile health app; 3) a fall risk app, Steady-MS, was developed and its usability was tested; and 4) the feasibility of home-based procedures for using Steady-MS was determined. Results suggest that smartphone accelerometry can assess postural control in pwMS. This information was used to develop an algorithm to measure overall fall risk in pwMS and was then incorporated into Steady-MS. Steady-MS was found to be usable among MS users and feasible to use in the home setting. The results from this project demonstrate that pwMS can independently assess their fall risk with Steady-MS in their homes. For the first time, pwMS are equipped to self-assess their fall risk and can monitor and manage their risk. Home-based assessments also opens the potential to offer individualized and targeted treatments to prevent falls. Ultimately, Steady-MS increases access to home-based assessments to reduce falls and improve functional independence for those with MS

The effect of acute caffeine ingestion on physical performance in elite European competitive soccer match-play

The present study examined the effect of acute caffeine ingestion (150 mg) on the physical performance of elite European soccer players during official competitive match-play. The current investigation was a parallel-group design that collated data from a cohort of 19 male outfield players from an elite European soccer team (mean ± SD, age 26 ± 4 years; weight 80.5 ± 8.1 kg; height 1.83 ± 0.07 m; body-fat 10.8 ± 0.7%). Players were classified and matched by position and grouped accordingly: centre defender (CD) n = 5, wide defender (WD) n = 3, centre midfield (CM) n = 7, wide forward (WF) n = 2, and centre forward (CF) n = 2. For all performance variables, the mean values were compared in caffeine consumers vs. non consumers using independent-sample t-tests, with significance set at p < .05. Cohen’s d was used to quantify the effect size, and was interpreted as trivial (<0.2), small (0.2-0.5), medium (0.5-0.8), and large (>0.8). For all examined variables, there were trivial or small non-significant (p > .05) trivial or small differences between caffeine consumers and non-consumers. The findings of the present research did not confirm the study hypothesis, once running and accelerometry-based variables did not improve with the caffeine ingestion of 150 mg. Therefore, the caffeine supplement used in this study is not suggested for improving performance in the variables analysed

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

DEVELOPMENT AND CROSS-VALIDATION OF A PREDICTION EQUATION FOR ESTIMATING STEP COUNT IN INDIVIDUALS WITH TRANSTIBIAL AMPUTATION

Outcome measures can be utilized to assess physical function in controlled settings, but do not provide a comprehensive view of free-living mobility for individuals with transtibial amputation (TTA). We sought to expand upon established clinical-based outcome measures by developing and cross validating two equations for predicting daily steps. The relationship between health state predictors and performance on 1) the Timed Up and Go (TUG) Test, and 2) the Prosthetic Limb User’s Survey of Mobility (PLUS-M) was also assessed via the model predictions. Adults with TTA were assigned activPAL and Fitbit accelerometers to wear for seven days. Participant data were randomly separated into training (n = 80) and testing (n = 26) groups. LASSO regression with 3-fold cross validation was implemented to construct each equation according to a participant’s health state, TUG Test, L Test of Functional Mobility, and PLUS-M data. Each equation’s validity was assessed in the testing group. An inverse relationship was noted between daily steps and TUG Test performance and higher PLUS-M T-scores were associated with greater daily steps. The equation overestimated steps for those with significantly low daily steps and underestimated steps for those with significantly high daily steps, which is to be expected given the nature of linear regression. We also assessed the validity of the Fitbit Inspire 3 for assessing steps among individuals with TTA. Daily step data were compared between the Fitbit Inspire 3 and the activPAL 3. The Fitbit overestimated physical activity by estimating higher daily steps compared to the activPAL. Because of the significant mean differences between the devices, the activPAL and Fitbit are not interchangeable for estimating steps in this group. The results will be interpreted and explored in the context of prosthetic rehabilitation and underscore the importance of personalized mobility assessments and interventions aimed at improving the free-living mobility of individuals with TTA

Behavioral Privacy Risks and Mitigation Approaches in Sharing of Wearable Inertial Sensor Data

Wrist-worn inertial sensors in activity trackers and smartwatches are increasingly being used for daily tracking of activity and sleep. Wearable devices, with their onboard sensors, provide appealing mobile health (mHealth) platform that can be leveraged for continuous and unobtrusive monitoring of an individual in their daily life. As a result, an adaptation of wrist-worn devices in many applications (such as health, sport, and recreation) increases. Additionally, an increasing number of sensory datasets consisting of motion sensor data from wrist-worn devices are becoming publicly available for research. However, releasing or sharing these wearable sensor data creates serious privacy concerns of the user. First, in many application domains (such as mHealth, insurance, and health provider), user identity is an integral part of the shared data. In such settings, instead of identity privacy preservation, the focus is more on the behavioral privacy problem that is the disclosure of sensitive behaviors from the shared sensor data. Second, different datasets usually focus on only a select subset of these behaviors. But, in the event that users can be re-identified from accelerometry data, different databases of motion data (contributed by the same user) can be linked, resulting in the revelation of sensitive behaviors or health diagnoses of a user that was neither originally declared by a data collector nor consented by the user. The contributions of this dissertation are multifold. First, to show the behavioral privacy risk in sharing the raw sensor, this dissertation presents a detailed case study of detecting cigarette smoking in the field. It proposes a new machine learning model, called puffMarker, that achieves a false positive rate of 1/6 (or 0.17) per day, with a recall rate of 87.5%, when tested in a field study with 61 newly abstinent daily smokers. Second, it proposes a model-based data substitution mechanism, namely mSieve, to protect behavioral privacy. It evaluates the efficacy of the scheme using 660 hours of raw sensor data collected and demonstrates that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors. Finally, it analyzes the risks of user re-identification from wrist-worn sensor data, even after applying mSieve for protecting behavioral privacy. It presents a deep learning architecture that can identify unique micro-movement pattern in each wearer\u27s wrists. A new consistency-distinction loss function is proposed to train the deep learning model for open set learning so as to maximize re-identification consistency for known users and amplify distinction with any unknown user. In 10 weeks of daily sensor wearing by 353 participants, we show that a known user can be re-identified with a 99.7% true matching rate while keeping the false acceptance rate to 0.1% for an unknown user. Finally, for mitigation, we show that injecting even a low level of Laplace noise in the data stream can limit the re-identification risk. This dissertation creates new research opportunities on understanding and mitigating risks and ethical challenges associated with behavioral privacy

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies.

Objectives Studies that assess all three dimensions of the integrative 24-hour physical behaviour (PB) construct, namely, intensity, posture/activity type and biological state, are on the rise. However, reviews on validation studies that cover intensity, posture/activity type and biological state assessed via wearables are missing. Design Systematic review. The risk of bias was evaluated by using the QUADAS-2 tool with nine signalling questions separated into four domains (ie, patient selection/study design, index measure, criterion measure, flow and time). Data sources Peer-reviewed validation studies from electronic databases as well as backward and forward citation searches (1970-July 2021). Eligibility criteria for selecting studies Wearable validation studies with children and adolescents (age <18 years). Required indicators: (1) study protocol must include real-life conditions; (2) validated device outcome must belong to one dimension of the 24-hour PB construct; (3) the study protocol must include a criterion measure; (4) study results must be published in peer-reviewed English language journals. Results Out of 13 285 unique search results, 76 articles with 51 different wearables were included and reviewed. Most studies (68.4%) validated an intensity measure outcome such as energy expenditure, but only 15.9% of studies validated biological state outcomes, while 15.8% of studies validated posture/activity type outcomes. We identified six wearables that had been used to validate outcomes from two different dimensions and only two wearables (ie, ActiGraph GT1M and ActiGraph GT3X+) that validated outcomes from all three dimensions. The percentage of studies meeting a given quality criterion ranged from 44.7% to 92.1%. Only 18 studies were classified as 'low risk' or 'some concerns'. Summary Validation studies on biological state and posture/activity outcomes are rare in children and adolescents. Most studies did not meet published quality principles. Standardised protocols embedded in a validation framework are needed. PROSPERO registration number CRD42021230894