An IoT Sensing Platform and Serious Game for Remote Martial Arts Training.

We propose a system for self-supported martial arts training using an IoT sensing platform and Serious Game that can also be extended for general sports training. In martial arts, it is important that the practitioner is correctly performing each technique to accurately learn and prevent injury. A common stance in all martial arts, but especially in Shaolin Kung Fu, is the horse stance or Mabu. With the pandemic, many more people adopted remote training without the presence of a professional trainer to give advice. Our developed LifeMat system, which is a novel IoT pressure-sensitive training mat, uses pressure maps and pattern recognition to accurately classify key martial arts postures, provide feedback on form, and detect when the user performs the technique incorrectly. This is presented in the form of a Serious Game we have developed named Kung Future that focuses on the Mabu stance as a case study. We tested 14 participants with three different feedback conditions and demonstrated that, on average, participants had higher performance, duration, engagement, and motivation when game feedback was active. Furthermore, user responses from the surveys suggested positive feedback for real-world and long-term use and scalability.

Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion.

We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system's performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method's performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.

Genetic interaction of DISC1 and Neurexin in the development of fruit fly glutamatergic synapses.

Originally identified at the breakpoint of a (1;11)(q42.1; q14.3) chromosomal translocation in a Scottish family with a wide range of mental disorders, the DISC1 gene has been a focus of intensive investigations as an entry point to study the molecular mechanisms of diverse mental dysfunctions. Perturbations of the DISC1 functions lead to behavioral changes in animal models, which are relevant to psychiatric conditions in patients. In this work, we have expressed the human DISC1 gene in the fruit fly (Drosophila melanogaster) and performed a genetic screening for the mutations of psychiatric risk genes that cause modifications of DISC1 synaptic phenotypes at the neuromuscular junction. We found that DISC1 interacts with dnrx1, the Drosophila homolog of the human Neurexin (NRXN1) gene, in the development of glutamatergic synapses. While overexpression of DISC1 suppressed the total bouton area on the target muscles and stimulated active zone density in wild-type background, a partial reduction of the dnrx1 activity negated the DISC1-mediated synaptic alterations. Likewise, overexpression of DISC1 stimulated the expression of a glutamate receptor component, DGLURIIA, in wild-type background but not in the dnrx1 heterozygous background. In addition, DISC1 caused mislocalization of Discs large, the Drosophila PSD-95 homolog, in the dnrx1 heterozygous background. Analyses with a series of domain deletions have revealed the importance of axonal localization of the DISC1 protein for efficient suppression of DNRX1 in synaptic boutons. These results thus suggest an intriguing converging mechanism controlled by the interaction of DISC1 and Neurexin in the developing glutamatergic synapses.