On effectiveness of lossless compression in transferring mHealth data files

Abstract—The health and fitness data traffic originating on mobile devices has been continually increasing, with an exponen-tial increase in the number of personal wearable devices and mobile health monitoring applications. Lossless data compression can increase throughput, reduce latency, and achieve energy-efficient communication between personal devices and the cloud. This paper experimentally explores the effectiveness of common compression utilities on mobile devices when uploading and downloading a representative mHealth data set. Based on the results of our study, we develop recommendations for effective data transfers that can assist mHealth application developers. Keywords—mobile sensing; health monitoring; wearable devic-es; data communication. I

Quantifying Benefits of Lossless Compression Utilities on Modern Smartphones

Abstract — The data traffic originating on mobile computing devices has been growing exponentially over the last several years. Lossless data compression and decompression can be es-sential in increasing communication throughput, reducing com-munication latency, achieving energy-efficient communication, and making effective use of available storage. This paper experi-mentally evaluates several compression utilities and configura-tions on a modern smartphone. We characterize each utility in terms of its compression ratio, compression and decompression throughput, and energy efficiency for representative use cases. We find a wide variety of energy costs associated with data com-pression and decompression and provide practical guidelines for selecting the most energy efficient configurations for each use case. For data transfers over WLAN, the best configurations provide a 2.1-fold and 2.7-fold improvement in energy efficiency for compressed uploads and downloads, respectively, when com-pared to uncompressed data transfers. For data transfers over a mobile broadband network, the best configurations provide a 2.7-fold and 3-fold improvement in energy efficiency for com-pressed uploads and downloads, respectively.1 Index Terms — Mobile computing, Measurement techniques

Smart Button: A wearable system for assessing mobility in elderly

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-Up-and-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. Keywords—mobile sensing; health monitoring; wearable devices; timed-up-and-go test; 30-second chair stand test. I

An Environment for Automated Power Measurements on Mobile Computing Platforms

Mobile computing devices such as smartphones, tablet computers, and e-readers have become the dominant personal computing platforms. Energy efficiency is a prime design requirement for mobile device manufacturers and smart application developers alike. Runtime power measurements on mobile platforms provide insights that can eventually lead to more energy-efficient operation. In this paper we describe mPowerProfile- an environment for automated power measurements of programs running on a mobile development platform. We discuss mPowerProfile’s main functions and its utilization in several example studies based on the Pandaboard and Raspberry Pi platforms

An mHealth Tool Suite for Mobility Assessment

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