An Activity Monitor for Diabetic Individuals

An activity monitor that diabetic individuals can wear continuously will provide important information on how these individuals should make adjustments to their exercise, diet, and insulin dosage in order to maintain a healthy lifestyle. The device is composed of both heart rate sensing components and components to measure the magnitude of physical movement. The energy expenditure is calculated using an algorithm that continuously adjusts depending on the type of activity. The system display provides the carbohydrates burned in order to be adjunctive to carbohydrate counting, a common technique used for glucose management

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

A wearable real-time system for physical activity recognition and fall detection

This thesis work designs and implements a wearable system to recognize physical activities and detect fall in real time. Recognizing people’s physical activity has a broad range of applications. These include helping people maintaining their energy balance by developing health assessment and intervention tools, investigating the links between common diseases and levels of physical activity, and providing feedback to motivate individuals to exercise. In addition, fall detection has become a hot research topic due to the increasing population over 65 throughout the world, as well as the serious effects and problems caused by fall. In this work, the Sun SPOT wireless sensor system is used as the hardware platform to recognize physical activity and detect fall. The sensors with tri-axis accelerometers are used to collect acceleration data, which are further processed and extracted with useful information. The evaluation results from various algorithms indicate that Naive Bayes algorithm works better than other popular algorithms both in accuracy and implementation in this particular application. This wearable system works in two modes: indoor and outdoor, depending on user’s demand. Naive Bayes classifier is successfully implemented in the Sun SPOT sensor. The results of evaluating sampling rate denote that 20 Hz is an optimal sampling frequency in this application. If only one sensor is available to recognize physical activity, the best location is attaching it to the thigh. If two sensors are available, the combination at the left thigh and the right thigh is the best option, 90.52% overall accuracy in the experiment. For fall detection, a master sensor is attached to the chest, and a slave sensor is attached to the thigh to collect acceleration data. The results show that all falls are successfully detected. Forward, backward, leftward and rightward falls have been distinguished from standing and walking using the fall detection algorithm. Normal physical activities are not misclassified as fall, and there is no false alarm in fall detection while the user is wearing the system in daily life

Signal processing for estimating energy expenditure of elite athletes using triaxial accelerometers

Fitness development of elite athletes requires an understanding of physiological factors such as athlete energy expenditure (EE). For athletes involved in football at the elite level, it is necessary to understand the energy demands during competition to develop training regimes. By identifying an appropriate EE estimator in triaxial accelerometer data, in conjunction with identifying sources of inter-athlete variance in that estimator, signal processing was developed to extract the estimator. In this system, low-power signal processing was implemented to extract both the EE estimator and other information of physiological and statistical interestGriffith Sciences, Griffith School of EngineeringFull Tex

Workplace performance monitoring: analysing the combination of physiological and environmental sensory inputs

The intent of this study was to investigate the combination of body physiological monitoring and the monitoring of the physical workplace as well as measuring worker/workplace performance through a questionnaire and activity log. We believed that these three data streams can be analyzed to reveal a strong correlation among them. The introduction of body physiological monitoring represents a new initiative in our quest for understanding the complex man-environment relationship

Activity and Health Status Monitoring System

Physical activity monitoring represents an important tool in supporting/encouraging vulnerable persons in their struggle to recover from surgery or long term illness promoting a healthy lifestyle. The paper proposes a smart, low power activity monitoring platform capable to acquire data from 4 inertial sensor modules placed on human body, temporarily store it on a mobile phone for real time data display or on a server for long term data analysis

Ultra-Low Power Sensor Devices for Monitoring Physical Activity and Respiratory Frequency in Farmed Fish

Integration of technological solutions aims to improve accuracy, precision and repeatability in farming operations, and biosensor devices are increasingly used for understanding basic biology during livestock production. The aim of this study was to design and validate a miniaturized tri-axial accelerometer for non-invasive monitoring of farmed fish with re-programmable schedule protocols. The current device (AE-FishBIT v.1s) is a small (14 mm × 7 mm × 7 mm), stand-alone system with a total mass of 600 mg, which allows monitoring animals from 30 to 35 g onwards. The device was attached to the operculum of gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax) juveniles for monitoring their physical activity by measurements of movement accelerations in x- and y-axes, while records of operculum beats (z-axis) served as a measurement of respiratory frequency. Data post-processing of exercised fish in swimming test chambers revealed an exponential increase of fish accelerations with the increase of fish speed from 1 body-length to 4 body-lengths per second, while a close relationship between oxygen consumption (MO2) and opercular frequency was consistently found. Preliminary tests in free-swimming fish kept in rearing tanks also showed that device data recording was able to detect changes in daily fish activity. The usefulness of low computational load for data pre-processing with on-board algorithms was verified from low to submaximal exercise, increasing this procedure the autonomy of the system up to 6 h of data recording with different programmable schedules. Visual observations regarding tissue damage, feeding behavior and circulating levels of stress markers (cortisol, glucose, and lactate) did not reveal at short term a negative impact of device tagging. Reduced plasma levels of triglycerides revealed a transient inhibition of feed intake in small fish (sea bream 50–90 g, sea bass 100–200 g), but this disturbance was not detected in larger fish. All this considered together is the proof of concept that miniaturized devices are suitable for non-invasive and reliable metabolic phenotyping of farmed fish to improve their overall performance and welfare. Further work is underway for improving the attachment procedure and the full device packaging

Sensing solutions for improving the performance, health and wellbeing of small ruminants

Diversity of production systems and specific socio-economic barriers are key reasons explaining why the implementation of new technologies in small ruminants, despite being needed and beneficial for farmers, is harder than in other livestock species. There are, however, helpful peculiarities where small ruminants are concerned: the compulsory use of electronic identification created a unique scenario in Europe in which all small ruminant breeding stock became searchable by appropriate sensing solutions, and the largest small ruminant population in the world is located in Asia, close to the areas producing new technologies. Notwithstanding, only a few research initiatives and literature reviews have addressed the development of new technologies in small ruminants. This Research Reflection focuses on small ruminants (with emphasis on dairy goats and sheep) and reviews in a non-exhaustive way the basic concepts, the currently available sensor solutions and the structure and elements needed for the implementation of sensor-based husbandry decision support. Finally, some examples of results obtained using several sensor solutions adapted from large animals or newly developed for small ruminants are discussed. Significant room for improvement is recognized and a large number of multiple-sensor solutions are expected to be developed in the relatively near future

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