Abstract-A key challenge for mobile health is to develop new technology that can assist individuals in maintaining a healthy lifestyle by keeping track of their everyday behaviors. Smartphones embedded with a wide variety of sensors are enabling a new generation of personal health applications that can actively monitor, model and promote wellbeing. Automated wellbeing tracking systems available so far have focused on physical fitness and sleep and often require external non-phone based sensors. In this work, we take a step towards a more comprehensive smartphone based system that can track activities that impact physical, social, and mental wellbeing namely, sleep, physical activity, and social interactions and provides intelligent feedback to promote better health. We present the design, implementation and evaluation of BeWell, an automated wellbeing app for the Android smartphones and demonstrate its feasibility in monitoring multi-dimensional wellbeing. By providing a more complete picture of health, BeWell has the potential to empower individuals to improve their overall wellbeing and identify any early signs of decline.

The idea of continuously monitoring well-being using mobile-sensing systems is gaining popularity. In-situ measurement of human behavior has the potential to overcome the short comings of gold-standard surveys that have been used for decades by the medical community. However, current sensing systems have mainly focused on tracking physical health; some have approximated aspects of mental health based on proximity measurements but have not been compared against medically accepted screening instruments. In this paper, we show the feasibility of a multi-modal mobile sensing system to simultaneously assess mental and physical health. By continuously capturing fine grained motion and privacy-sensitive audio data, we are able to derive different metrics that reflect the results of commonly used surveys for assessing well-being by the medical community. In addition, we present a case study that highlights how errors in assessment due to the subjective nature of the responses could potentially be avoided by continuous sensing and inference of social interactions and physical activities.

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PURPOSE Automated systems able to infer detailed measures of a person's social interactions and physical activities in their natural environments could lead to better understanding of factors infl uencing well-being. We assessed the feasibility of a wireless mobile device in measuring sociability and physical activity in older adults, and compared results with those of traditional questionnaires.METHODS This pilot observational study was conducted among a convenience sample of 8 men and women aged 65 years or older in a continuing care retirement community. Participants wore a waist-mounted device containing sensors that continuously capture data pertaining to behavior and environment (accelerometer, microphone, barometer, and sensors for temperature, humidity, and light). The sensors measured time spent walking level, up or down an elevation, and stationary (sitting or standing), and time spent speaking with 1 or more other people. The participants also completed 4 questionnaires: the 36-Item Short Form Health Survey (SF-36), the Yale Physical Activity Survey (YPAS), the Center for Epidemiologic Studies-Depression (CES-D) scale, and the Friendship Scale.RESULTS Men spent 21.3% of their time walking and 64.4% stationary. Women spent 20.7% of their time walking and 62.0% stationary. Sensed physical activity was correlated with aggregate YPAS scores (r 2 = 0.79, P = .02). Sensed time speaking was positively correlated with the mental component score of the SF-36 (r 2 = 0.86, P = .03), and social interaction as assessed with the Friendship Scale (r 2 = 0.97, P = .002), and showed a trend toward association with CES-D score (r 2 = -0.75, P = .08). In adjusted models, sensed time speaking was associated with SF-36 mental component score (P = .08), social interaction measured with the Friendship Scale (P = .045), and CES-D score (P = .04).CONCLUSIONS Mobile sensing of sociability and activity is well correlated with traditional measures and less prone to biases associated with questionnaires that rely on recall. Using mobile devices to collect data from and monitor older adult patients has the potential to improve detection of changes in their health. INTRODUCTIONA n important goal of community health programs is to improve the overall quality of life by promoting cognitive, physical, and social/ emotional well-being.1,2 Everyday behaviors are often refl ective of physical and physiologic health states, and can be predictive of future health problems. The standard practice for collecting behavioral data in the health sciences relies on observational data collected in laboratory settings or through periodic surveys or self-reports. These proxy measures have several major limitations, however: (1) the time and resource requirements are too great to simultaneously gather data from a large number of individuals; (2) the measurements are prone to considerable bias, and the manual and sporadic recording of information often fails to capture the fi ner details of behavior that may be important; and (3) 345 ME A SUR...

The demand for wearable technologies has grown tremendously in recent years. Wearable antennas are used for various applications, in many cases within the context of wireless body area networks (WBAN). In WBAN, the presence of the human body poses a significant challenge to the wearable antennas. Specifically, such requirements are required to be considered on a priority basis in the wearable antennas, such as structural deformation, precision, and accuracy in fabrication methods and their size. Various researchers are active in this field and, accordingly, some significant progress has been achieved recently. This article attempts to critically review the wearable antennas especially in light of new materials and fabrication methods, and novel designs, such as miniaturized button antennas and miniaturized single and multi-band antennas, and their unique smart applications in WBAN. Finally, the conclusion has been drawn with respect to some future directions.