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Development of a real time activity monitoring Android application utilizing SmartStep

Published on Aug 1, 2016 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
· DOI :10.1109/EMBC.2016.7591089
Nagaraj Hegde7
Estimated H-index: 7
(UA: University of Alabama),
Edward L. Melanson40
Estimated H-index: 40
(Anschutz Medical Campus),
Edward Sazonov27
Estimated H-index: 27
(UA: University of Alabama)
Abstract
Footwear based activity monitoring systems are becoming popular in academic research as well as consumer industry segments. In our previous work, we had presented developmental aspects of an insole based activity and gait monitoring system-SmartStep, which is a socially acceptable, fully wireless and versatile insole. The present work describes the development of an Android application that captures the SmartStep data wirelessly over Bluetooth Low energy (BLE), computes features on the received data, runs activity classification algorithms and provides real time feedback. The development of activity classification methods was based on the the data from a human study involving 4 participants. Participants were asked to perform activities of sitting, standing, walking, and cycling while they wore SmartStep insole system. Multinomial Logistic Discrimination (MLD) was utilized in the development of machine learning model for activity prediction. The resulting classification model was implemented in an Android Smartphone. The Android application was benchmarked for power consumption and CPU loading. Leave one out cross validation resulted in average accuracy of 96.9% during model training phase. The Android application for real time activity classification was tested on a human subject wearing SmartStep resulting in testing accuracy of 95.4%.
  • References (15)
  • Citations (11)
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References15
Newest
Nov 9, 2015 in BIBM (Bioinformatics and Biomedicine)
#1Nagaraj Hegde (UA: University of Alabama)H-Index: 7
#2Edward Sazonov (UA: University of Alabama)H-Index: 27
Insole based wearable sensors are becoming popular in applications such as gait and physical activity monitoring, energy expenditure estimation, in providing biofeedback, fall risk and others. In the existing systems, the insole needs to be taken out of the shoe to recharge the battery, which is not a convenient task. The existing systems are application specific and can be used for a limited purpose. However, the desired system functionality for the shoe based wearable systems depends on factor...
11 CitationsSource
Aug 1, 2015 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Nagaraj Hegde (UA: University of Alabama)H-Index: 7
#2George D. Fulk (Clarkson University)H-Index: 20
Last. Edward Sazonov (UA: University of Alabama)H-Index: 27
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Regaining the ability to walk is a major rehabilitation goal after a stroke. Recent research suggests that, in people with stroke, task-oriented and intensive rehabilitation strategies can drive cortical reorganization and increase activity levels. This paper describes development and pilot testing of a novel wearable device for Real-Time Gait and Activity Improving Telerehabilitation (RT-GAIT), designed for use with such rehabilitation strategies. The RT-GAIT provides auditory or tactile feedba...
6 CitationsSource
#1Edward Sazonov (UA: University of Alabama)H-Index: 27
#2Nagaraj Hegde (UA: University of Alabama)H-Index: 7
Last. Nadezhda Sazonova (UA: University of Alabama)H-Index: 11
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17 CitationsSource
#1Ty Ferguson (UniSA: University of South Australia)H-Index: 1
#2Alex V. Rowlands (UniSA: University of South Australia)H-Index: 22
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Background Technological advances have seen a burgeoning industry for accelerometer-based wearable activity monitors targeted at the consumer market. The purpose of this study was to determine the convergent validity of a selection of consumer-level accelerometer-based activity monitors.
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AB Background: Many consumer-based monitors are marketed to provide personal information on the levels of physical activity and daily energy expenditure (EE), but little or no information is available to substantiate their validity. Purpose: This study aimed to examine the validity of EE estimates from a variety of consumer-based, physical activity monitors under free-living conditions. Methods: Sixty (26.4 +/- 5.7 yr) healthy males (n = 30) and females (n = 30) wore eight different types of act...
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#1Nagaraj Hegde (UA: University of Alabama)H-Index: 7
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Shoe-mounted wearable sensors can be used in applications, such as activity monitoring, gait analysis, post-stroke rehabilitation, body weight measurements and energy expenditure studies. Such wearable sensors typically require the modification or alteration of the shoe, which is not typically feasible for large populations without the direct involvement of shoe manufacturers. This article presents an insole-based wearable sensor (SmartStep) that has its electronics fully embedded into a generic...
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DANNECKER, K. L., N. A. SAZONOVA, E. L. MELANSON, E. S. SAZONOV, and R. C. BROWNING. A Comparison of Energy Expenditure Estimation of Several Physical Activity Monitors. Med. Sci. Sports Exerc., Vol. 45, No. 11, pp. 2105–2112, 2013. Introduction: Accurately and precisely estimating free-living energy expenditure (EE) is important for monitoring energy balance and quantifying physical activity. Recently, single and multisensor devices have been developed that can classify physical activities, pot...
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In our previous research we developed a SmartShoe-a shoe based physical activity monitor that can reliably differentiate between major postures and activities, accurately estimate energy expenditure of individuals, measure temporal gait parameters, and estimate body weights. In this paper we present the development of the next stage of the SmartShoe evolution-SmartStep, a physical activity monitor that is fully integrated into an insole, maximizing convenience and social acceptance of the monito...
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The ability to provide real time feedback concerning a person's activity level and energy expenditure can be beneficial for improving activity levels of individuals. Examples include biofeedback systems used for body weight and physical activity management and biofeedback systems for rehabilitation of stroke patients. A critical aspect of any such system is being able to accurately classify data in real-time so that active and timely feedback can be provided. In the paper we demonstrate feasibil...
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