Selective joint motion recognition using multi sensor for salat learning

Over the past few years, there has been significant attention given on motion recognition in computer vision as it has a wide range of potential applications that can be further developed. Hence, a wide variety of algorithms and techniques has been proposed to develop human motion recognition systems for the benefit of the human. Salat—an essential ritual in Muslim daily life which helps them be good Muslims—is not solely about the spiritual act, but it also involves the physical movements in which it has to be done according to its code of conduct. The existing motion recognition proposed for computing applications for salat movement is unsuitable as the movement in salat must be performed in accordance to the rules and procedures stipulated, the accuracy and sequence. In addition, tracking all skeleton joints does not contribute equally toward activity recognition as well as it is also computationally intensive. The current salat recognition focuses on recognizing main movements and it does not cover the whole cycle of salat activity. Besides, using a wearable sensor is not natural in performing salat since the user needs to give absolute concentration during salat activity. The research conducted was based on the intersections of technological development and Muslim spiritual practices. This study has been developed utilizing dual-sensor cameras and a special sensor prayer mat that has the ability to cooperate in recognizing salat movement and identifying the error in the movement. With the current technology in depth cameras and software development kits, human joint information is available to locate the joint position. Only important joints with the significant movement were selected to be tracked to perform real-time motion recognition. This selective joint algorithm is computationally efficient and offers good recognition accuracy in real-time. Once the features have been constructed, the Hidden Markov Model classifier was utilized to train and test the algorithm. The algorithm was tested on a purposely built dataset of depth videos recorded using a Kinect camera. This motion recognition system was designed based on the salat activity to recognize the user movement and his error rate, which will later be compared with the traditional tutor-based methodology. Subsequently, an evaluation comprising 25 participants was conducted utilizing usability testing methods. The experiment was conducted to evaluate the success score of the user’s salat movement recognition and error rate. Besides, user experience and subjective satisfaction toward the proposed system have been considered to evaluate user acceptance. The results showed that the evaluation of the proposed system was significantly different from the traditional tutor-based method evaluation. Results indicated a significant difference (p < 0.05) in success score and user’s error rate between the proposed system and traditional tutor-based methodology. This study also depicted that the proposed motion recognition system had successfully recognized salat movement and evaluated user error in salat activity, offering an alternative salat learning methodology. This motion identification system appears to offer an alternate learning process in a variety of study domains, not just salat movement activity

Crowdsourcing Just in Time Knowledge at Workplace

The use of technology across a number of domains and facets is widespread. It is predicted by Forrester that almost half (42%) of the entire world’s population, by the end of 2015, will own a smart phone. Furthermore, during the last ten years, there has been much development in the communication arena as a direct result of smart mobile technologies, including within the work setting, thus facilitating a greater degree of communication and information-sharing capacity in work communities. Nonetheless, it remains that not all the features and tools offered by this technology are utilised, which predominantly is owing to the lack of insight and understanding of users. Accordingly, we argue that people sharing knowledge in the workplace are sharing all the knowledge they are aware of in the most effective way, because it is shared in the situation where they naturally experience problems -at the workplace. Owing to the universal nature inherent in this technology, it is considered pivotal that smart phone technology goes hand-in-hand with intrinsic support. Importantly, however, if not altogether lacking, this is very often inadequate. However, adopting mobile technology within the workplace setting can give rise to challenges that impact user behaviour and performance. Four studies were conducted with the aim of examining how employees address and manage problems on a smart mobile device (SMD) and accordingly aim at overcoming the issue. The first three studies considered provides valuable input for the researcher that was recognised as required in the fourth research. The third study was carried out amongst 90 participants located in two countries, using internet connectivity, as a case study. Confidence and frustration have previously been connected with technology competence, but this was not applied to a workplace scenario during problem-solving, when users are assigned an unfamiliar smart mobile device. This research focuses on identifying the link between workplace users’ levels of confidence and frustration when seeking to independently solve problems whilst completing familiar tasks on new smart mobile devices. A detailed video analysis of users’ attitudes and behaviour during problem- solving was conducted, highlighting a correlation between attitudes and behaviour towards completing a task. When reviewing and considering the findings from the first researches, the criteria for a universal crowdsourcing solution were identified. In the final of the studies, users across different levels of technology experiences and from varying job roles in different departments in a firm were brought together to form a collaborative community referred to as YourSpace designed and implemented for this thesis. To this end, the subjects were grouped across three progressive levels of a knowledge management framework devised for this specific study, namely Pedagogy (engagement), Andragogy (cultivation) and Heutagogy (realisation) levels. The employees of Malta International Airport were permitted to utilise YourSpace for a one- week period, during which time there was an assessment of its adoption within the work setting. Methodology validation in this thesis was carried out through the considered design of a tablet-based research instrument that encompassed a characteristic facilitating knowledge-capture. This was achieved through taking YourSpace and accordingly utilising its peer-to-peer support communities. An innovative method is introduced through improving modern-day global technology in a number of ways: firstly, by further expanding works carried out in the social media domain, specifically by capturing Just in Time knowledge when seeking to overcome obstacles in the work environment; secondly, by providing a crowdsourcing instrument with the capacity to capture Just in Time knowledge in an organic work setting through gaining insight into individuals’ characteristics and their within-community interactions throughout the process; and thirdly, by examining the behaviours and perspectives of users when seeking to overcome common issues experienced when utilising an unfamiliar device. The results highlighted provide a crowdsourced Just in Time support solution, which could prove pivotal in overcoming problems through the provision of a collaborative framework that supports the gathering of knowledge that is not dependent on technology experience