Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.Comment: submitted to the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON

Capturing accelerometer outputs in healthy volunteers under normal and simulated-pathological conditions using ML classifiers

Wearable devices offer a possible solution for acquiring objective measurements of physical activity. Most current algorithms are derived using data from healthy volunteers. It is unclear whether such algorithms are suitable in specific clinical scenarios, such as when an individual has altered gait. We hypothesized that algorithms trained on healthy population will result in less accurate results when tested in individuals with altered gait. We further hypothesized that algorithms trained on simulated-pathological gait would prove better at classifying abnormal activity.We studied healthy volunteers to assess whether activity classification accuracy differed for those with healthy and simulated-pathological conditions. Healthy participants (n=30) were recruited from the University of Leeds to perform nine predefined activities under healthy and simulated-pathological conditions. Activities were captured using a wrist-worn MOX accelerometer (Maastricht Instruments, NL). Data were analyzed based on the Activity-Recognition-Chain process. We trained a Neural-Network, Random-Forests, k-Nearest-Neighbors (k-NN), Support-Vector-Machines (SVM) and Naive Bayes models to classify activity. Algorithms were trained four times; once with 'healthy' data, and once with 'simulated-pathological data' for each of activity-type and activity-task classification. In activity-type instances, the SVM provided the best results; the accuracy was 98.4% when the algorithm was trained and then tested with unseen data from the same group of healthy individuals. Accuracy dropped to 52.8% when tested on simulated-pathological data. When the model was retrained with simulated-pathological data, prediction accuracy for the corresponding test set was 96.7%. Algorithms developed on healthy data are less accurate for pathological conditions. When evaluating pathological conditions, classifier algorithms developed using data from a target sub-population can restore accuracy to above 95%.Clinical Relevance - This method remotely establishes health-related data of objective outcome measures of activities of daily living

Advanced multi-sensor platform for chronic disease home monitoring

Nowadays chronic diseases affect an ever-growing segment of population in developed countries; and the management of such kind of diseases requires a huge amount of resources. Chronic Heart Failure, Chronic Obstructive Pulmonary Disease, Diabetes, etc. are the main causes of hospitalization for elderly people, and considering the general aging of population this may lead sustainability problems in the near future. In the last years, clinicians and administrators have identified the telemedicine as strategy to improve the patient management, ensuring both a decreasing of hospital admissions and improving the patient's quality of life. This paper presents a complete system for the management of the healthcare information related to the chronic patient treatment, integrating three main points: a configurable multi-sensor platform for the acquisition and transmission of vital signs, a dedicated server for the provisioning of centralized telemedicine services and the possibility of synchronizing with the electronic health record

Accelerometery as a measure of modifiable physical activity in high-risk elderly preoperative patients: a prospective observational pilot study.

OBJECTIVES: To use wrist-worn accelerometers (Axivity AX3) to establish normative physical activity (PA) and acceptability data for the high-risk elderly preoperative population, to assess whether PA could be modified by a prehabilitation intervention as part of routine care, to assess any correlation between accelerometer-measured PA and self-reported PA and to assess the acceptability of wearing wrist-worn accelerometers in this population. STUDY DESIGN: Prospective, observational, pilot study. SETTING: Single National Health Service Hospital. PARTICIPANTS: Frail patients≥65 years awaiting major surgery referred to a multidisciplinary preoperative clinic at which they received a routine intervention aimed at improving their PA. 35 patients were recruited. Average age 79.9 years (SD=5.6). PRIMARY OUTCOMES: Normative PA data measured as a mean daily Euclidean norm minus one (ENMO) in milli-gravitational units (mg). SECONDARY OUTCOMES: Measure PA levels (mg) following a routine preoperative intervention. Determine correlation between patient-reported PA (measured using the Physical Activity Scale for the Elderly) and accelerometer-measured PA (mg). Assess acceptability of wearing a wrist-worn accelerometer measured using Visual Analogue Scale (VAS) questionnaire and device wear time (hours). RESULTS: Median baseline daily PA was 14.3 mg (IQR 9.75-22.04) with an improvement in PA detected following the intervention (median ENMO post intervention 20.91 mg (IQR 14.83-27.53), p=0.022). There was no significant correlation between accelerometer-measured and self-reported PA (baseline ρ=0.162 (p=0.4), post intervention ρ=-0.144 (p=0.5)). We found high acceptability ratings (median score of 10/10 on VAS, IQR 8-10) and wear-time compliance (163.2 hours (IQR 150-167.5) preintervention and 166.1 hours (IQR 162.5-167) post intervention). CONCLUSIONS: Accelerometery is acceptable to this population and increases in PA levels measured following an unoptimised routine clinical intervention which indicates that health behavioural change interventions may be successful during the preoperative period. Accelerometers may therefore be a useful tool to design and validate interventions for improving PA in this setting. TRIAL REGISTRATION NUMBER: NCT03737903

Daily physical activity patterns during the early stage of Alzheimer’s disease

Background - Alzheimer’s disease (AD) is a neurodegenerative disease that results in severe disability. Very few studies have explored changes in daily physical activity patterns during early stages of AD when components of physical function and mobility may be preserved. Methods - Patients with mild AD and controls (n=92) recruited from the University of Kansas Alzheimer’s Disease Center Registry, wore the Actigraph GT3X+ for seven days, and provided objective physical function (VO2 max) and mobility data. Using multivariate linear regression, we explored whether individuals with mild AD had different daily average and diurnal physical activity patterns compared to controls independent of non-cognitive factors that may affect physical activity, including physical function and mobility. Results - We found that mild AD was associated with less moderate-intensity physical activity (p<0.05), lower peak activity (p<0.01), and lower physical activity complexity (p<0.05) particularly during the morning. Mild AD was not associated with greater sedentary activity or less lower-intensity physical activity across the day after adjusting for non-cognitive covariates. Conclusions - These findings suggest that factors independent of physical capacity and mobility may drive declines in moderate-intensity physical activity, and not lower-intensity or sedentary activity, during the early stage of AD. This underscores the importance of a better mechanistic understanding of how cognitive decline and AD pathology impact physical activity. Findings emphasize the potential value of designing and testing time-of-day specific physical activity interventions targeting individuals in the early stages of AD, prior to significant declines in mobility and physical function

Study of Multi-Classification of Advanced Daily Life Activities on SHIMMER Sensor Dataset

Today the field of wireless sensors have the dominance in almost every person’s daily life. Therefore researchers are exasperating to make these sensors more dynamic, accurate and high performance computational devices as well as small in size, and also in the application area of these small sensors. The wearable sensors are the one type which are used to acquire a person’s behavioral characteristics. The applications of wearable sensors are healthcare, entertainment, fitness, security and military etc. Human activity recognition (HAR) is the one example, where data received from wearable sensors are further processed to identify the activities executed by the individuals. The HAR system can be used in fall detection, fall prevention and also in posture recognition. The recognition of activities is further divided into two categories, the un-supervised learning and the supervised learning. In this paper we first discussed some existing wearable sensors based HAR systems, then briefly described some classifiers (supervised learning) and then the methodology of how we applied the multiple classification techniques using a benchmark data set of the shimmer sensors placed on human body, to recognize the human activity. Our results shows that the methods are exceptionally accurate and efficient in comparison with other classification methods. We also compare the results and analyzed the accuracy of different classifiers

Investigating optimal accelerometer placement for energy expenditure prediction in children using a machine learning approach

Accurate measurement of energy expenditure (EE) is imperative for identifying and targeting health-associated implications. Whilst numerous accelerometer-based regression equations to predict EE have been developed, there remains little consensus regarding optimal accelerometer placement. Therefore, the purpose of the present study was to validate and compare artificial neural networks (ANNs) developed from accelerometers worn on various anatomical positions, and combinations thereof, to predict EE.Twenty-seven children (15 boys; 10.8  ±  1.1 years) participated in an incremental treadmill test and 30 min exergaming session wearing a portable gas analyser and nine ActiGraph GT3X+  accelerometers (chest and left and right wrists, hips, knees, and ankles). Age and sex-specific resting EE equations (Schofield) were used to estimate METs from the oxygen uptake measures. Using all the data from both exergames, incremental treadmill test and the transition period in between, ANNs were created and tested separately for each accelerometer and for combinations of two or more using a leave-one-out approach to predict EE compared to measured EE. Six features (mean and variance of the three accelerometer axes) were extracted within each 15 s window as inputs in the ANN. Correlations and root mean square error (RMSE) were calculated to evaluate prediction accuracy of each ANN, and repeated measures ANOVA was used to statistically compare accuracy of the ANNs.All single-accelerometer ANNs and combinations of two-, three-, and four-accelerometers performed equally (r  =  0.77–0.82), demonstrating higher correlations than the 9-accelerometer ANN (r  =  0.69) or the Freedson linear regression equation (r  =  0.75). RMSE did not differ between single-accelerometer ANNs or combinations of two, three, or four accelerometers (1.21–1.31 METs), demonstrating lower RMSEs than the 9-accelerometer ANN (1.46 METs) or Freedson equation (1.74 METs).These findings provide preliminary evidence that ANNs developed from single accelerometers mounted on various anatomical positions demonstrate equivalency in the accuracy to predict EE in a semi-structured setting, supporting the use of ANNs in improving EE prediction accuracy compared with linear regression