Ambulatory monitoring of cumulative free-living activity

Recent Advances in Wearable Optical Sensor Automation Powered by Battery versus Skin-like Battery-Free Devices for Personal Healthcare-A Review

Currently, old-style personal Medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patient discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin are used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Basic physiological signals comprise a lot of health-related data. The estimation of critical physiological characteristics, such as pulse inconstancy or variability using photoplethysmography (PPG) and oxygen saturation in arterial blood using pulse oximetry, is possible by utilizing an analysis of the pulsatile component of the bloodstream. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. Flexible skin-like sensing devices have accomplished several functionalities previously inaccessible for typical sensing devices due to their deformability, lightness, portability, and flexibility. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors, which brings a breakthrough in wearable sensing automation.

Relationships Between Physical Activity and Bone Density in People Living with HIV: Results from the SATURN-HIV Study

We conducted a cross-sectional secondary analysis of baseline data from the SATURN-HIV study (N = 147; 78% male, 68% Black, median body mass index [BMI] 26.72 kg/m2, 13% with osteopenia, HIV-1 RNA < 1,000 copies/mL, stable antiretroviral therapy [ART]) to explore the relationship between physical activity (PA) and bone mineral density (BMD). We measured self-reported minutes of PA and BMD in the overall sample and subgroups based on national recommendations (≥150 minutes/week). Forty-one (28%) participants met recommended PA levels. Higher intensity PA was associated with higher BMD at the total hip (r = 0.27, p = .09; n = 41; 28%) and lumbar spine (r = 0.32, p < .05), and predicted higher BMD at the hip (p < .01; controlling for age, BMI, ART). Lumbar spine BMD did not retain significance in the regression model. Moderate-to-high intensity PA could prevent or mitigate excessive bone loss in people living with HIV.

Wearable and Implantable Sensors for Biomedical Applications

Mobile health technologies offer great promise for reducing healthcare costs and improving patient care. Wearable and implantable technologies are contributing to a transformation in the mobile health era in terms of improving healthcare and health outcomes and providing real-time guidance on improved health management and tracking. In this article, we review the biomedical applications of wearable and implantable medical devices and sensors, ranging from monitoring to prevention of diseases, as well as the materials used in the fabrication of these devices and the standards for wireless medical devices and mobile applications. We conclude by discussing some of the technical challenges in wearable and implantable technology and possible solutions for overcoming these difficulties.

A review of wearable sensors and systems with application in rehabilitation

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

A biomedical sensor system for real-time monitoring of astronauts' physiological parameters during extra-vehicular activities

OBJECTIVE

To design and test an embedded biomedical sensor system that can monitor astronauts' comprehensive physiological parameters, and provide real-time data display during extra-vehicle activities (EVA) in the space exploration.

METHODS

An embedded system was developed with an array of biomedical sensors that can be integrated into the spacesuit. Wired communications were tested for physiological data acquisition and data transmission to a computer mounted on the spacesuit during task performances simulating EVA sessions.

RESULTS

The sensor integration, data collection and communication, and the real-time data monitoring were successfully validated in the NASA field tests.

CONCLUSIONS

The developed system may work as an embedded system for monitoring health status during long-term space mission.

Clinical applications of wearable technology

An important factor contributing to the process involved in choosing a rehabilitation intervention is the assessment of its impact on the real life of patients. Therapists and physicians have to infer the effectiveness of rehabilitation approaches from observations performed in the clinical setting and from patients' feedback. Recent advances in wearable technology have provided means to supplement the information gathered using tools based on patient's direct observation as well as interviews and questionnaires. A new generation of wearable sensors and systems has recently become available thus providing clinical personnel with a "window of observation" in the home and community settings. These tools allow one to capture patients' activity level and exercise compliance, facilitate titration of medications in chronic patients, and provide means to assess the ability of patients to perform specific motor activities. In this paper, we review recent advances in the field of wearable technology and provide examples of application of this technology in rehabilitation.

Episodic sampling: towards energy-efficient patient monitoring with wearable sensors

Energy efficiency presents a critical design challenge in wireless, wearable sensor technology, mainly because of the associated diagnostic objectives required in each monitoring application. In order to maximize the operating lifetime during real-life monitoring and maintain sufficient classification accuracy, the wearable sensors require hardware support that allows dynamic power control on the sensors and wireless interfaces as well as monitoring algorithms to control these components intelligently. This paper introduces a context-aware sensing technique known as episodic sampling - a method of performing context classification only at specific time instances. Based on Additive-Increase/Multiplicative-Decrease (AIMD), episodic sampling demonstrates an energy reduction of 85 percent with a loss of only 5 percent in classification accuracy in our experiment.

Free-living physical activity in COPD: assessment with accelerometer and activity checklist

To assess physical activity and disability in chronic obstructive pulmonary disease (COPD), we evaluated the use of an accelerometer and checklist to measure free-living physical activity. Seventeen males with stable COPD completed a daily activity checklist for 14 days. Ten subjects concurrently wore an Actiped accelerometer (FitSense, Southborough, Massachussetts) that records steps per day. Regression models assessed relationships between steps per day, number of daily checklist activities performed, and clinical measures of COPD status. The average steps per day ranged from 406 to 4,856. The median intrasubject coefficient of variation for steps per day was 0.52 (interquartile range [IQR] 0.41-0.58) and for number of daily checklist activities performed was 0.28 (IQR 0.22-0.32). A higher number of steps per day was associated with a greater distance walked on the 6-minute walk test and better health-related quality of life. A higher number of daily checklist activities performed was associated with a higher force expiratory volume in 1 s percent predicted and lowerbody mass index, airflow obstruction, dyspnea, exercise capacity (BODE) index. Prospectively measuring free-living physical activity in COPD using an unobtrusive accelerometer and simple activity checklist is feasible. Low intrasubject variation was found in free-living physical activity, which is significantly associated with clinical measures of COPD status.

A review of smart homes- present state and future challenges

In the era of information technology, the elderly and disabled can be monitored with numerous intelligent devices. Sensors can be implanted into their home for continuous mobility assistance and non-obtrusive disease prevention. Modern sensor-embedded houses, or smart houses, cannot only assist people with reduced physical functions but help resolve the social isolation they face. They are capable of providing assistance without limiting or disturbing the resident's daily routine, giving him or her greater comfort, pleasure, and well-being. This article presents an international selection of leading smart home projects, as well as the associated technologies of wearable/implantable monitoring systems and assistive robotics. The latter are often designed as components of the larger smart home environment. The paper will conclude by discussing future challenges of the domain.

MEDIC: medical embedded device for individualized care

OBJECTIVE

Presented work highlights the development and initial validation of a medical embedded device for individualized care (MEDIC), which is based on a novel software architecture, enabling sensor management and disease prediction capabilities, and commercially available microelectronic components, sensors and conventional personal digital assistant (PDA) (or a cell phone).

METHODS AND MATERIALS

In this paper, we present a general architecture for a wearable sensor system that can be customized to an individual patient's needs. This architecture is based on embedded artificial intelligence that permits autonomous operation, sensor management and inference, and may be applied to a general purpose wearable medical diagnostics.

RESULTS

A prototype of the system has been developed based on a standard PDA and wireless sensor nodes equipped with commercially available Bluetooth radio components, permitting real-time streaming of high-bandwidth data from various physiological and contextual sensors. We also present the results of abnormal gait diagnosis using the complete system from our evaluation, and illustrate how the wearable system and its operation can be remotely configured and managed by either enterprise systems or medical personnel at centralized locations.

CONCLUSION

By using commercially available hardware components and software architecture presented in this paper, the MEDIC system can be rapidly configured, providing medical researchers with broadband sensor data from remote patients and platform access to best adapt operation for diagnostic operation objectives.

Lifeguard--a personal physiological monitor for extreme environments

Monitoring vital signs in applications that require the subject to be mobile requires small, lightweight, and robust sensors and electronics. A body-worn system should be unobtrusive, noninvasive, and easy-to-use. It must be able to log vital signs data for several hours as well as transmit it on demand in real-time using secure wireless technologies. The NASA Ames Research Center (Astrobionics) and Stanford University (National Center for Space Biological Technologies) are currently developing a wearable physiological monitoring system for astronauts, called LifeGuard, that meets all of the above requirements and is also applicable to clinical, home-health monitoring, first responder and military applications.

A multiparameter wearable physiologic monitoring system for space and terrestrial applications

A novel, unobtrusive and wearable, multiparameter ambulatory physiologic monitoring system for space and terrestrial applications, termed LifeGuard, is presented. The core element is a wearable monitor, the crew physiologic observation device (CPOD), that provides the capability to continuously record two standard electrocardiogram leads, respiration rate via impedance plethysmography, heart rate, hemoglobin oxygen saturation, ambient or body temperature, three axes of acceleration, and blood pressure. These parameters can be digitally recorded with high fidelity over a 9-h period with precise time stamps and user-defined event markers. Data can be continuously streamed to a base station using a built-in Bluetooth RF link or stored in 32 MB of on-board flash memory and downloaded to a personal computer using a serial port. The device is powered by two AAA batteries. The design, laboratory, and field testing of the wearable monitors are described.

Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data

Background

Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture.

Methods

A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD) undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging.

Results

Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks.

Conclusion

Hierarchical clustering methods are relevant to developing classifiers of motor activities from data recorded using wearable systems. They allow users to assess feasibility of a classification problem and choose architectures that maximize accuracy. By relying on this approach, the clinical importance of discriminating motor tasks can be easily taken into consideration while designing the classifier.

Advances in wearable technology and applications in physical medicine and rehabilitation

The development of miniature sensors that can be unobtrusively attached to the body or can be part of clothing items, such as sensing elements embedded in the fabric of garments, have opened countless possibilities of monitoring patients in the field over extended periods of time. This is of particular relevance to the practice of physical medicine and rehabilitation. Wearable technology addresses a major question in the management of patients undergoing rehabilitation, i.e. have clinical interventions a significant impact on the real life of patients? Wearable technology allows clinicians to gather data where it matters the most to answer this question, i.e. the home and community settings. Direct observations concerning the impact of clinical interventions on mobility, level of independence, and quality of life can be performed by means of wearable systems. Researchers have focused on three main areas of work to develop tools of clinical interest: 1)the design and implementation of sensors that are minimally obtrusive and reliably record movement or physiological signals, 2)the development of systems that unobtrusively gather data from multiple wearable sensors and deliver this information to clinicians in the way that is most appropriate for each application, and 3)the design and implementation of algorithms to extract clinically relevant information from data recorded using wearable technology. Journal of NeuroEngineering and Rehabilitation has devoted a series of articles to this topic with the objective of offering a description of the state of the art in this research field and pointing to emerging applications that are relevant to the clinical practice in physical medicine and rehabilitation.