• An approach to locating delayed activities in software processes

      Jin, Yun-Zhi; Zhou, Hua; Yang, Hong-Ji; Zhang, Sijing; Ge, Ji-Dong; Yunnan University; Key Laboratory for Software Engineering of Yunnan Province; Research Center of Cloud Computing of Yunnan Province; Bath Spa University; University of Bedfordshire; et al. (Chinese Academy of Sciences, 2017-09-21)
      Activity is now playing a vital role in software processes. To ensure the high-level efficiency of software processes, a key point is to locate those activities that own bigger resource occupation probabilities with respect to average execution time, called delayed activities, and then improve them. To this end, we firstly propose an approach to locating delayed activities in software processes. Furthermore, we present a case study, which exhibits the high-level efficiency of the approach, to concretely illustrate this new solution. Some beneficial analysis and reasonable modification are developed in the end.
    • Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: a systematic review

      Qi, Jun; Yang, Po; Waraich, Atif; Deng, Zhikun; Zhao, Youbing; Yang, Yun; Yunnan University; Liverpool John Moore University; University of Bedfordshire (Elsevier, 2018-09-26)
      Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.