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Browsing by Author "Hegde, Nagaraj"

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    A Comparative Review of Footwear-Based Wearable Systems
    (2016-08-10) Hegde, Nagaraj; Bries, Matthew; Sazonov, Edward; University of Alabama Tuscaloosa
    Footwear is an integral part of daily life. Embedding sensors and electronics in footwear for various different applications started more than two decades ago. This review article summarizes the developments in the field of footwear-based wearable sensors and systems. The electronics, sensing technologies, data transmission, and data processing methodologies of such wearable systems are all principally dependent on the target application. Hence, the article describes key application scenarios utilizing footwear-based systems with critical discussion on their merits. The reviewed application scenarios include gait monitoring, plantar pressure measurement, posture and activity classification, body weight and energy expenditure estimation, biofeedback, navigation, and fall risk applications. In addition, energy harvesting from the footwear is also considered for review. The article also attempts to shed light on some of the most recent developments in the field along with the future work required to advance the field.
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    Development and validation of an insole based wearable system for gait and activity monitoring
    (University of Alabama Libraries, 2017) Hegde, Nagaraj; Sazonov, Edward; University of Alabama Tuscaloosa
    Footwear based wearable sensors are used in applications such as activity monitoring, gait analysis, post-stroke rehabilitation, body weight measurements, and energy expenditure estimation. Such wearable sensors typically required the modification or alteration of the shoe, which is not typically feasible for large populations without the direct involvement of shoe manufacturers. This dissertation presents an insole-based wearable sensor (SmartStep) that has its electronics fully embedded into a generic insole, which can be used with a large variety of shoes and, thus, resolves the need for shoe modification. The SmartStep is an always-on electronic device that comprises of inertial sensors and resistive pressure sensors implemented around a system on chip with Bluetooth low energy (BLE) connectivity. The dissertation explains a novel Android application methodology to interface multiple BLE sensors. As a part of the research, SmartStep’s predecessor, SmartShoe, was tested in an application scenario of gait monitoring of children with cerebral palsy. Novel signal processing algorithms were developed to deal with the effect of orthotics on pressure sensors. Machine learning models were used to recognize 3 major activities (sitting, standing and walking) with greater than 95% accuracy. The gait parameter estimation resulted in average error less than 6% across a range of temporal gait parameters. The SmartStep device was utilized in a study for recognizing activities of daily living (ADL) in adults. An upper-body, wrist-worn sensor was also utilized in the same study to compare the effectiveness of upper and lower body-worn sensors in recognizing ADL. The indirect calorimetry data from a portable metabolic system (Cosmed K4b), collected in the same study, were used to develop energy expenditure (EE) estimation models. Two different sets of steady state activity branched models were developed, along with models for transition activities. The EE models were validated on the data from a room calorimeter. SmartStep was 90% accurate in recognizing a broad set of nine ADLs, including household activities; while having 6% validation error in estimating total energy expenditure. These results suggest that SmartStep is accurate in recognizing multiple activities of daily living and in energy expenditure estimation.
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    Posture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platform
    (IEEE, 2015) Sazonov, Edward; Hegde, Nagaraj; Browning, Raymond C.; Melanson, Edward L.; Sazonova, Nadezhda A.; University of Alabama Tuscaloosa; Colorado State University; University of Colorado Denver
    The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here, we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we compare the use of support vector machines (SVM), multinomial logistic discrimination (MLD), and multilayer perceptrons (MLP) for posture and activity classification followed by activity-branched EE estimation. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a 4-h stay in a room calorimeter. MLD and MLP demonstrated activity classification accuracy virtually identical to SVM (similar to 95%) while reducing the running time and the memory requirements by a factor of >10(3). Comparison of per-minute EE estimation using activity-branched models resulted in accurate EE prediction (RMSE = 0.78 kcal/min for SVM andMLD activity classification, 0.77 kcal/min for MLP versus RMSE of 0.75 kcal/min for manual annotation). These results suggest that low-power computational algorithms can be successfully used for real-time physical activity monitoring and EE estimation on a wearable platform.
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    SmartStep: A Fully Integrated, Low-Power Insole Monitor
    (2014-06-18) Hegde, Nagaraj; Sazonov, Edward; University of Alabama Tuscaloosa
    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 insole, which is usable with a large variety of shoes and, thus, resolves the need for shoe modification. The SmartStep is an always-on electronic device that comprises a 3D accelerometer, a 3D gyroscope and resistive pressure sensors implemented around a CC2540 system-on-chip with an 8051 processor core, Bluetooth low energy (BLE) connectivity and flash memory buffer. The SmartStep is wirelessly interfaced to an Android smart phone application with data logging and visualization capabilities. This article focuses on low-power implementation methods and on the method developed for reliable data buffering, alleviating intermittent connectivity resulting from the user leaving the vicinity of the smart phone. The conducted tests illustrate the power consumption for several possible usage scenarios and the reliability of the data retention method. The trade-off between the power consumption and supported functionality is discussed, demonstrating that SmartStep can be worn for more than two days between battery recharges. The results of the mechanical reliability test on the SmartStep indicate that the pressure sensors in the SmartStep tolerated prolonged human wear. The SmartStep system collected more than 98.5% of the sensor data, in real usage scenarios, having intermittent connectivity with the smart phone.