Novatchkov, Hristo, and Arnold Baca. "Artificial intelligence in sports on the example of weight training." Journal of sports science & medicine 12.1 (2013): 27.
A research by Novatchkov and Baca explore the utilization of artificial intelligence in sports particularly in weight training. The study concentrated on the application of pattern recognition techniques for the assessment of completed aerobics on training machines. The weight machines were fitted with cable force sensors, which assisted in data collection (Novatchkov and Baca 1). In so doing, it facilitates the determination of important force and displacement determinants in the process of training (Novatchkov and Baca 1). Practically, the execution of such practices and techniques is vital for the examination of the value of the execution. Importantly, the use of AI techniques in training exercises would greatly assist coaches and athletes because they help to measure automatically performances especially on weight training equipment hence offering them with instant advice (Novatchkov and Baca 3).
The study participants included 15 sportsmen who were inexperienced engaging in 3-5 sets of physical exercise using a leg-press machine. The machine had AI techniques where professional trainers were employed in classification and assessment processes by examining the video documented performances (Novatchkov and Baca 9). Based on the data collected, the article noted that AI is a favourable industry for analysing sports-related activities.
Zang, Yaping, et al. "Advances of flexible pressure sensors toward artificial intelligence and health care applications." Materials Horizons 2.2 (2015): 140-156.
The article by Zang et al. attempted to determine the application of artificial intelligence in health care monitoring. In fact, it reviewed the use of pressure sensors in the healthcare sector especially on assessment of intraocular pressure and blood pressure. The research noted that pressure sensors had the ability to produce signals under a particular pressure and operate in signal transduction (Zang et al. 140). Importantly, the feature facilitates the successful utilization of pressure sensors in AI. In this regard, active sensing tools have been invented, which are important applications for electronic skin.
Pressure sensors utilize AI system, which enable mobile bio-monitoring in health care and medical diagnostics. They are made up of organic materials that offer flexibility in pursuit of devices, which are foldable and lightweight (Zang et al. 147). The e-skin systems are extremely sensitive to touch hence they are able to monitor the health of an individual.
Ali, Yasir Hassan, R. Abd Rahman, and Raja Ishak Raja Hamzah. "Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review." Journal Technology 69.2 (2014).
A research by Ali, Rahman, and Hamzah studied the use of artificial intelligence and acoustic emission (AE) technique in fault diagnosis and monitoring machine condition. The AI process is largely useful owing to its sensitivity in identifying small cracks. For this reason, the AI techniques and AE process could be utilized for regular maintenance (Ali, Rahman, and Hamzah 1). The application of artificial intelligence in maintenance of machines has been beneficial because they enhance machine availability, productivity, and reduction of maintenance costs especially in in