Parkinsonian gait is associated with life-threatening consequences such as fall risk in Parkinson patients. Conventional Parkinsonian gait analysis heavily relies on expensive sensors and human labor. In this work, we propose a sensor-free end-to-end system which enables the automated and accurate Parkinsonian gait detection and analysis upon the videos recorded by pervasive cameras. Specifically, we leverage Deep Learning technologies to extract the human skeleton in the video frame and address the camera random angle challenge. By analyzing the gait features, we train a classifier based on a binary decision tree. Out of 16 Parkinsonian gait and 13 healthy gait videos, our system is able to detect the Parkinsonian Gait with 93.75% accuracy and healthy gait with 100% accuracy.
As the next-generation manufacturing driven force, 3D printing technology is having a transformative effect on various industrial domains and has been widely applied in a broad spectrum of applications. It also progresses towards other versatile fields with portable battery-powered 3D printers working on a limited energy budget. While reducing manufacturing energy is an essential challenge in industrial sustainability and national economics, this growing trend motivates us to explore the energy consumption of the 3D printer for the purpose of energy efficiency. To this end, we perform an in-depth analysis of energy consumption in commercial, off-the-shelf 3D printers from an instruction-level perspective. We build an instruction-level energy model and an energy profiler to analyze the energy cost during the fabrication process. From the insights obtained by the energy profiler, we propose and implement a cross-layer energy optimization solution, called 3DGates, which spans the instruction-set, the compiler and the firmware. We evaluate 3DGates over 338 benchmarks on a 3D printer and achieve an overall energy reduction of 25%.
Long-term rehabilitation opportunities are critical for millions of individuals with chronic upper limb motor deficits striving to improve their motor performance through self-managed rehabilitation programs. However, there is minimal professional support of rehabilitation across the lifespan. In this paper, we introduce an upper extremity rehabilitation system, the Quality of Movement Feedback-Oriented Measurement System (QM-FOrMS), by integrating cost-effective portable sensors and clinically verified motion quality analysis towards individuals with upper limb motor deficits. Specifically, QM-FOrMS is comprised of an eTextile pressure sensitive mat, named Smart Mat, a sensory can, named Smart Can, and a mobile device. A personalizable and adaptive upper limb rehabilitation program is developed, including both unilateral and bilateral functional activities which can be selected from a list or custom designed to further tailor the program to the individual. Quantitative evaluation of the motor performance from the QM-FOrMS is derived from fine-grained kinematic measurements. We ran a pilot study with three groups, including five baseline subjects (i.e., healthy young adults), six older adults and four individuals with movement impairment. The experimental results show that QM-FOrMS can provide the detailed feature during the unattended rehabilitation exercise, and proposed metrics can distinguish the evaluation results across group.
Embedded database engines such as SQLite provide a convenient data persistence layer and have spread along with the applications using them to many types of systems, including interactive devices such as smartphones. Android, the most widely-distributed smart-phone platform, both uses SQLite internally and provides interfaces encouraging apps to use SQLite to store their own private structured data. As similar functionality appears in all major mobile operating systems, embedded database performance affects the response times and resource consumption of billions of smartphones and the millions of apps that run on them—making it more important than ever to characterize smartphone embedded database workloads. To do so, we present results from an experiment which recorded SQLite activity on 11 Android smartphones during one month of typical usage. Our analysis shows that Android SQLite usage produces queries and access patterns quite different from canonical server workloads. We argue that evaluating smartphone embedded databases will require anew benchmarking suite and we use our results to outline some of its characteristics.