Kinetic Chain Exercise Intervention Improved Spiking Consistency and Kinematics in Volleyball Players With Scapular Dyskinesis.

ABSTRACT: Chang, C-C, Chang, C-M, and Shih, Y-F. Kinetic chain exercise intervention improved spiking consistency and kinematics in volleyball players with Scapular Dyskinesis. J Strength Cond Res 36(10): 2844-2852, 2022-Scapular dyskinesis (SD) is a common problem among volleyball players with chronic shoulder pain. This randomized controlled study examined the effectiveness of kinetic chain (KC) training on neuromuscular performance of the scapula and trunk during volleyball spikes. Forty volleyball players with SD and chronic shoulder pain received 4 weeks of KC training or the conventional shoulder exercise training (CT). Shoulder pain was assessed using the visual analogue scale (VAS) every week. The kinematics and muscle activation of the shoulder and upper trunk, and proprioceptive feedback magnitude (PFM) for scapular movement consistency, were recorded at the maximum shoulder flexion (T1) and ball contact (T2) during spiking tasks. The two-way repeated measures analysis of variances was used to assess the between-group differences before and after the intervention. The results showed a significant time by group interaction for the upper trunk rotation ( p < 0.001) and PFM ( p = 0.03) at T2. The post-hoc test indicated that the KC group significantly increased contralateral rotation of the upper trunk (9.63 ± 4.19° vs. -4.25 ± 10.05°), and improved movement consistency (error: 8.88 ± 11.52° vs. 19.73 ± 12.79°) at T2 compared with the CT group. Significant time effects were also identified for VAS, scapular upward rotation (T1 and T2), upper trunk contralateral side-bending and PFM at T1, and upper trunk contralateral rotation at T2. In conclusion, both KC and CT training would relieve shoulder pain and improve scapular and trunk movement, whereas the KC program was more effective for increasing scapular movement consistency and upper trunk rotation during volleyball spikes.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.