Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach

Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O-2 concentrators (POC) in LTOT is being expanded. However, the issue of oxygen titration is not always properly addressed, since POCs rely on proper use by patients. The robustness of algorithms and the limited reliability of current oximetry sensors are hindering the effectiveness of new approaches to closed-loop POCs based on the feedback of blood oxygen saturation. In this study, a novel intelligent portable oxygen concentrator (iPOC) is described. The presented iPOC is capable of adjusting the O-2 flow automatically by real-time classifying the intensity of a patient's physical activity (PA). It was designed with a group of patients with COPD and stable chronic respiratory failure. The technical pilot test showed a weighted accuracy of 91.1% in updating the O-2 flow automatically according to medical prescriptions, and a general improvement in oxygenation compared to conventional POCs. In addition, the usability achieved was high, which indicated a significant degree of user satisfaction. This iPOC may have important benefits, including improved oxygenation, increased compliance with therapy recommendations, and the promotion of PA

Energy expenditure prediction via a footwear-based physical activity monitor: accuracy and comparison to other devices

2011 Summer.Includes bibliographical references.Accurately estimating free-living energy expenditure (EE) is important for monitoring or altering energy balance and quantifying levels of physical activity. The use of accelerometers to monitor physical activity and estimate physical activity EE is common in both research and consumer settings. Recent advances in physical activity monitors include the ability to identify specific activities (e.g. stand vs. walk) which has resulted in improved EE estimation accuracy. Recently, a multi]sensor footwear-based physical activity monitor that is capable of achieving 98% activity identification accuracy has been developed. However, no study has compared the EE estimation accuracy for this monitor and compared this accuracy to other similar devices. PURPOSE: To determine the accuracy of physical activity EE estimation of a footwear-based physical activity monitor that uses an embedded accelerometer and insole pressure sensors and to compare this accuracy against a variety of research and consumer physical activity monitors. METHODS: Nineteen adults (10 male, 9 female), mass: 75.14 (17.1) kg, BMI: 25.07(4.6) kg/m2 (mean (SD)), completed a four hour stay in a room calorimeter. Participants wore a footwear-based physical activity monitor, as well as three physical activity monitoring devices used in research: hip]mounted Actical and Actigraph accelerometers and a multi-accelerometer IDEEA device with sensors secured to the limb and chest. In addition, participants wore two consumer devices: Philips DirectLife and Fitbit. Each individual performed a series of randomly assigned and ordered postures/activities including lying, sitting (quietly and using a computer), standing, walking, stepping, cycling, sweeping, as well as a period of self-selected activities. We developed branched (i.e. activity specific) linear regression models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. RESULTS: The shoe-based device was not significantly different than the mean measured EE (476(20) vs. 478(18) kcal) (Mean(SE)), respectively, and had the lowest root mean square error (RMSE) by two]fold (29.6 kcal (6.19%)). The IDEEA (445(23) kcal) and DirecLlife (449(13) kcal) estimates of EE were also not different than the measured EE. The Actigraph, Fitbit and Actical devices significantly underestimated EE (339 (19) kcal, 363(18) kcal and 383(17) kcal, respectively (p<.05)). Root mean square errors were 62.1 kcal (14%), 88.2 kcal(18%), 122.2 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for DirectLife, IDEEA, Actigraph, Actical and Fitbit respectively. CONCLUSIONS: The shoe based physical activity monitor was able to accurately estimate EE. The research and consumer physical activity monitors tested have a wide range of accuracy when estimating EE. Given the similar hardware of these devices, these results suggest that the algorithms used to estimate EE are primarily responsible for their accuracy, particularly the ability of the shoe-based device to estimate EE based on activity classifications

Physical Activity and Mental Well-being in a Cohort Aged 60–64 Years

Introduction: Although evidence suggests physical activity (PA) may be associated with mental well-being at older ages, it is unclear whether some types of PA are more important than others. The purpose of this study is to investigate associations of monitored total PA under free-living conditions, self-reported leisure-time PA (LTPA), and walking for pleasure with mental well-being at age 60–64 years. Methods: Data on 930 (47%) men and 1,046 (53%) women from the United Kingdom MRC National Survey of Health and Development collected in 2006–2011 at age 60–64 were used in 2013–2014 to test the associations of PA (PA energy expenditure and time spent in different intensities of activity assessed using combined heart rate and acceleration monitors worn for 5 days, self-reported LTPA, and walking for pleasure) with the Warwick-Edinburgh Mental Well-being Scale (WEMWBS; range, 14–70). Results: In linear regression models adjusted for gender, long-term limiting illness, smoking, employment, socioeconomic position, personality, and prior PA, those who walked for >1 hour/week had mean WEMWBS scores 1.47 (95% CI=0.60, 2.34) points higher than those who reported no walking. Those who participated in LTPA at least five times/month had WEMWBS scores 1.25 (95% CI=0.34, 2.16) points higher than those who did not engage in LTPA. There were no statistically significant associations between free-living PA and WEMWBS scores. Conclusions: In adults aged 60–64 years, participation in self-selected activities such as LTPA and walking are positively related to mental well-being, whereas total levels of free-living PA are not

EVALUATION OF ACCELEROMETER-BASED ACTIVITY MONITORS TO ASSESS ENERGY EXPENDITURE OF MANUAL WHEELCHAIR USERS WITH SPINAL CORD INJURY

A primary objective of the study was to determine the validity of a SenseWear (SW) activity monitor (AM) in assessing Energy Expenditure (EE) of manual wheelchair users with spinal cord Injury (SCI) while resting and performing three types of physical activities including wheelchair propulsion, arm-ergometer exercise, and deskwork. A secondary objective of the study was to build and validate a new EE prediction model for a SW AM for the physical activities performed in the study. A tertiary objective was to examine the relationship between the criterion EE and three activity monitors including the ActiGraph, the RT3 on arm, and RT3 on waist. Ten manual wheelchair users with SCI were recruited to participate in this pilot study.The results indicate that EE estimated by SenseWear AM with the default EE equationfor resting was close (0.2%) to the criterion EE in manual wheelchair users with SCI. However, the SW AM overestimated EE during deskwork, wheelchair propulsion and arm-ergometry exercise by 6.5%, 105% and 32%, respectively.From the investigation, we found that the EE estimated by SW AM using the new regression equation model significantly improved its performance in manual wheelchair users with SCI. The Intraclass Correlation Coefficient of EE estimated by SW using new prediction equation and the criterion EE were excellent (0.90) and moderate (0.74) with percent errors reduced to 17.4% and 7.0% for wheelchair propulsion and arm-ergometry exercise, respectively. The new prediction equation for SW AM was able to differentiate and discriminate (sensitive)EE estimation in physical activities like wheelchair propulsion and arm-ergometer exercises in manual wheelchair users with SCI indicating that it has a potential to be used in manual wheelchair users with SCI.In addition, the variance explained by RT3 (R2 = 0.68, p<0.001) on arm and the ActiGraph (R2 = 0.59, p<0.001) on the wrist wrist indicate that AMs placed on an arm or wrist may be able to better predict EE compared to the AM on the waist

Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information

Non-invasive fitness assessment in horses:Integrating wearables and machine learning

The field of human sports has advanced significantly with the integration of machine learning and sensors for performance analysis. However, sport horses have not benefited equally from technological advancements due to their inability to provide feedback, such as verbal expressions of fatigue or difficulty.Veterinarians and researchers traditionally interpret equine well-being through methods like verbal encouragement, facial expressions, and blood sample analysis. These methods are either subjective or invasive, causing stress and disruption during training. Accurate and reliable fitness parameter values are essential to avoid overtraining and injuries, necessitating a more effective and less intrusive approach.This PhD thesis aims to revolutionize sport horse training by using wearable inertial sensors and state-of-the-art machine learning to enhance performance and prevent injuries. The study is divided into nine chapters, each contributing to the overall goal of improving equine fitness and well-being.Each chapter begins with a literature review to identify gaps and challenges in equine fitness and performance. Inertial sensors were chosen for their ability to capture a wide range of real-time motion data. The sensors were placed on various parts of the horse’s body, including the head, neck, shoulders, back, and legs. Data were collected during various training and competition scenarios to evaluate the system's effectiveness.The results demonstrated that the system could accurately capture and analyze a broad spectrum of motion data, providing valuable insights for trainers and riders. This technology can improve fitness and prevent injuries in sport horses by offering practical tools for assessing equine fitness outside of laboratory settings.This thesis makes significant contributions to equine research by leveraging wearable sensor technology and machine learning to enhance our understanding of equine fitness, performance, and well-being. The findings are valuable not only to the scientific community but also to the broader equestrian world, promoting the welfare of sport horses and the sustainability of the equestrian industry

Classification Accuracy of the Wrist-Worn GENEA Accelerometer During Structured Activity Bouts: A Cross-Validation Study

Purpose: The purpose of this study is to determine whether the left wrist cutpoints of Esliger et al., for the triaxial GENEA accelerometer, are accurate for predicting intensity categories during 14 different activities including; treadmill-based, home and office, and sport activities. Methods: 130 adults wore a GENEA accelerometer on their left wrist while performing various lifestyle activities. The Oxycon Mobile Portable Metabolic Unit was used to measure oxygen uptake during each activity. Statistical analysis used Spearman’s rank correlations to determine the relationship between measured and estimated intensity classifications. Cross tabulation tables were constructed to describe under or over estimation of misclassified activities, and one-way chi-squares were used to test whether the accuracy rate of each activity differed from 80%. Results: For all activities the GENEA accelerometer-based physical activity monitor explained 41.1% of the energy expenditure. The GENEA correctly classified 52.8% of observations when all activities were combined. Five of the 14 activities showed no statistical difference in physical activity intensity classification estimation when compared to 80% accuracy, with 1 activity (treadmill jogging) showing statistically greater accuracy than 80%. For the remainder of the activities, the GENEA showed less than 80% accuracy for predicting intensity. Conclusion: Cross-validation of the proposed GENEA left wrist cutpoints classified the majority of activities performed significantly below the accuracy rate of 80%. Researchers should be cautious when applying the Esliger et al. cutpoints to a different population and activities not tested by those investigators

A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring

Characteristics of physical activity are indicative of one’s mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies