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Book ChapterDOI

Achieving Wellness by Monitoring the Gait Pattern with Behavioral Intervention for Lifestyle Diseases

TL;DR: Result suggests that with the adaption of accurate classification process, system could be useful for controlled exercise monitoring or for daily activity monitoring, which is working at low-power level with affordable wearable technology in achieving wellness.
Abstract: Obesity is becoming one of the prevalent lifestyle diseases across the globe; need to be deal with behavioral intervention through self-management and motivation in line with rigorous physical exercise. When it is a matter to handle obesity to regain the health, parameters like: self-motivation, self-control, guided treatment, counseling, monitored exercise, and medical assistance are essential in consideration list. Paper proposes the model encompassing three-dimensional care including nutritional intake, counseling, and gait monitoring during exercise. Considering the effect of obesity on biomechanics of foot, gait pattern analysis of obese person provides greater information regarding variations in spatio-temporal parameters. Ever-increasing contribution of behavioral intervention will maintain the line of action in the perfect direction. A selection of accelerometer, gyroscope, and electromyography sensors is appropriate for the cause to derive basic hardware. MSP430 processor and ZigBee module are used for processing information and establishing communication. Within close proximity and placement of nodes at a different level, nodes are able to achieve 90–94% packet delivery ratio in actual environment compare to the 100% packet delivery in simulation environment. Result suggests that with the adaption of accurate classification process, system could be useful for controlled exercise monitoring or for daily activity monitoring, which is working at low-power level with affordable wearable technology in achieving wellness.
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Journal ArticleDOI
TL;DR: The opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multiomics data integration, and social media are discussed.
Abstract: Objective: This paper discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorized into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multiomics data integration, and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm toward preventative, predictive, personalized, and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realizing the goal of sustainable healthcare systems.

283 citations

Journal ArticleDOI
TL;DR: Monitoring the activities of daily living (ADLs) and detection of deviations from previous patterns is crucial to assessing the ability of an elderly person to live independently in their community and in early detection of upcoming critical situations.
Abstract: Monitoring the activities of daily living (ADLs) and detection of deviations from previous patterns is crucial to assessing the ability of an elderly person to live independently in their community and in early detection of upcoming critical situations. ?Aging in place? for an elderly person is one key element in ambient assisted living (AAL) technologies.

206 citations

Journal ArticleDOI
TL;DR: The results suggest that insole-based activity sensors may present a compelling alternative or companion to commonly used wrist devices.
Abstract: Automatic recognition of activities of daily living (ADL) is an important component in understanding of energy balance, quality of life, and other areas of health and well-being. In our previous work, we had proposed an insole-based activity monitor—SmartStep, designed to be socially acceptable and comfortable. The goals of the current study were: first, validation of SmartStep in recognition of a broad set of ADL; second, comparison of the SmartStep to a wrist sensor and testing these in combination; third, evaluation of SmartStep's accuracy in measuring wear noncompliance and a novel activity class (driving); fourth, performing the validation in free living against a well-studied criterion measure (ActivPAL, PAL Technologies); and fifth, quantitative evaluation of the perceived comfort of SmartStep. The activity classification models were developed from a laboratory study consisting of 13 different activities under controlled conditions. Leave-one-out cross validation showed 89% accuracy for the combined SmartStep and wrist sensor, 81% for the SmartStep alone, and 69% for the wrist sensor alone. When household activities were grouped together as one class, SmartStep performed equally well compared to the combination of SmartStep and wrist-worn sensor (90% versus 94%), whereas the accuracy of the wrist sensor increased marginally (73% from 69%). SmartStep achieved 92% accuracy in recognition of nonwear and 82% in recognition of driving. Participants then were studied for a day under free-living conditions. The overall agreement with ActivPAL was 82.5% (compared to 97% for the laboratory study). The SmartStep scored the best on the perceived comfort reported at the end of the study. These results suggest that insole-based activity sensors may present a compelling alternative or companion to commonly used wrist devices.

76 citations

Journal ArticleDOI
TL;DR: The results suggest that low-power computational algorithms can be successfully used for real-time physical activity monitoring and EE estimation on a wearable platform.
Abstract: 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 ( ∼ 95%) while reducing the running time and the memory requirements by a factor of >103. Comparison of per-minute EE estimation using activity-branched models resulted in accurate EE prediction (RMSE = 0.78 kcal/min for SVM and MLD 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.

43 citations

Journal ArticleDOI
22 May 2013
TL;DR: An overview of research regarding adult behavioral lifestyle intervention for obesity treatment is provided and methods to assist with meeting physical activity goals, such as shortening exercise bouts, using a pedometer, and having access to exercise equipment within the home are reviewed.
Abstract: This article provides an overview of research regarding adult behavioral lifestyle intervention for obesity treatment. We first describe two trials using a behavioral lifestyle intervention to induce weight loss in adults, the Diabetes Prevention Program (DPP) and the Look AHEAD (Action for Health in Diabetes) trial. We then review the three main components of a behavioral lifestyle intervention program: behavior therapy, an energy- and fat-restricted diet, and a moderate- to vigorous-intensity physical activity prescription. Research regarding the influence of dietary prescriptions focusing on macronutrient composition, meal replacements, and more novel dietary approaches (such as reducing dietary variety and energy density) on weight loss is examined. Methods to assist with meeting physical activity goals, such as shortening exercise bouts, using a pedometer, and having access to exercise equipment within the home, are reviewed. To assist with improving weight loss outcomes, broadening activity goals to include resistance training and a reduction in sedentary behavior are considered. To increase the accessibility of behavioral lifestyle interventions to treat obesity in the broader population, translation of efficacious interventions such as the DPP, must be undertaken. Translational studies have successfully altered the DPP to reduce treatment intensity and/or used alternative modalities to implement the DPP in primary care, worksite, and church settings; several examples are provided. The use of new methodologies or technologies that provide individualized treatment and real-time feedback, and which may further enhance weight loss in behavioral lifestyle interventions, is also discussed.

35 citations