Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Effects of water-based exercise on bone health of middle-aged and older adults:A systematic review and meta-analysis

Background: Age-related bone loss is a major health concern. Only exercises associated with high-impact and mechanical loading have been linked to a positive effect on bone turnover; however, these types of exercises may not always be appropriate for middle-aged and older adults due to physical decline or chronic disorders such as osteoarthritis. Water-based exercise (WBE) has been shown to affect different components of physical fitness, has lower risks of traumatic fracture, and applies less stress to joints. However, the effects of WBE on bone health are unclear. Objective: This study aimed to explore whether WBE is effective in preventing age-related bone deterioration in middle-aged and older adults. Methods: A search of relevant databases and the references of identified studies was performed. Critical narrative synthesis and meta-analyses were conducted. Results: Eleven studies, involving 629 participants, met all inclusion criteria. All participants were postmenopausal women. Eight studies compared WBE to a sedentary control group, and four studies had land-based exercise (LBE) participants as a comparison group. Meta-analyses revealed significant differences between WBE and control group in favor of WBE for changes in bone mineral density (BMD) at the lumbar spine (mean difference [MD] 0.03 g/cm2 ; 95% confidence interval [CI]: 0.01 to 0.05) and femoral neck (MD 0.04 g/cm2 ; 95% CI: 0.02 to 0.07). Significant differences were also revealed between WBE and LBE in favor of LBE for changes in lumbar spine BMD (MD -0.04 g/cm2 ; 95% CI: -0.06 to -0.02). However, there was no significant difference between WBE and LBE for changes in femoral neck BMD (MD -0.03 g/cm2 ; 95% CI: -0.08 to 0.01). Conclusion: WBE may have benefits with respect to maintaining or improving bone health in postmenopausal women but less benefit when compared to LBE. Further research is required on this topic

Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201