Abstract-Continuous advances in sensors, semiconductors, wireless networks, mobile and cloud computing enable the development of integrated wearable computing systems for continuous health monitoring. These systems can be used as a part of diagnostic procedures, in the optimal maintenance of chronic conditions, in the monitoring of adherence to treatment guidelines, and for supervised recovery. In this paper, we describe a wearable system called Smart Button designed to assess mobility of elderly. The Smart Button is easily mounted on the chest of an individual and currently quantifies the Timed-Upand-Go and 30-Second Chair Stand tests. These two tests are routinely used to assess mobility, balance, strength of the lower extremities, and fall risk of elderly and people with Parkinson's disease. The paper describes the design of the Smart Button, parameters used to quantify the tests, signal processing used to extract the parameters, and integration of the Smart Button into a broader mHealth system.

Modern smartphones integrate a growing number of inertial and environmental sensors that can enable the development of new mobile health applications. In this paper we introduce a suite of smartphone applications for assessing mobility in elderly population. The suite currently includes applications that automate and quantify the following standardized medical tests for assessing mobility: Timed-Up-and-Go (TUG), 30 Seconds Chair Stand Test (30SCS), and a 4-stage Balance Test (4SBT). For each smartphone application we describe its functionality and a list of parameters extracted by processing signals from smartphone's inertial sensors. The paper shows the results from studies conducted on geriatric patients for TUG tests and from studies conducted in the laboratory on healthy subjects for 30SCS and 4SBT tests.

Abstract:The assessment of mobility and functional impairments in the elderly is important for early detection and prevention of fall conditions. Falls create serious threats to health by causing disabling fractures that reduce independence in the elderly. Moreover, they exert heavy economic burdens on society due to high treatment costs. Modern smartphones enable the development of innovative mobile health (mHealth) applications by integrating a growing number of inertial and environmental sensors along with the ever-increasing data processing and communication capabilities. Mobility assessment is one of the promising mHealth application domains. In this paper, we introduce a suite of smartphone applications for assessing mobility in the elderly population. The suite currently includes smartphone applications that automate and quantify the following standardized medical tests for assessing mobility: Timed Up and Go (TUG), 30-Second Chair Stand Test (30SCS), and 4-Stage Balance Test (4SBT). For each application, we describe its functionality and a list of parameters extracted by processing signals from smartphone's inertial sensors. The paper shows the results from studies conducted on geriatric patients for TUG tests and from experiments conducted in the laboratory on healthy subjects for 30SCS and 4SBT tests.

Abstract-Data compression and decompression utilities can be critical in increasing communication throughput, reducing communication latencies, achieving energy-efficient communication, and making effective use of available storage. This paper experimentally evaluates several such utilities for multiple compression levels on systems that represent current mobile platforms. We characterize each utility in terms of its compression ratio, compression and decompression throughput, and energy efficiency. We consider different use cases that are typical for modern mobile environments. We find a wide variety of energy costs associated with data compression and decompression and provide practical guidelines for selecting the most energy efficient configurations for each use case. The best performing configurations provide 6-fold and 4-fold improvements in energy efficiency for compressed uploads and downloads over WLAN, respectively, when compared to uncompressed data transfers.

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