Implementation of Sequence-Based Classification Methods for Motion Assessment and Recognition in a Traditional Chinese Sport (Baduanjin)

The effect of Baduanjin exercise on the physical and mental health of college students: A randomized controlled trial

BACKGROUND AND PURPOSE

Around the world, college students physical and mental health is deteriorating. Finding practical ways to enhance college students health is crucial. One of the traditional Chinese Qigong exercises is Baduanjin, which is possibly one of the most efficient workout techniques out there. However, it is unknown how Baduanjin practice may affect college students health. The purpose of this study was to evaluate the effect of Qigong Baduanjin on the physical and mental health of female college freshmen.

MATERIALS AND METHODS

A total of 78 female college students were recruited and randomly divided into Baduanjin group or control group. Participants in the control group were told to keep their original habits of life and exercise. The Baduanjin exercise group received 12 weeks of Baduanjin exercise, 3 days a week and 1 hour a day. After the 12 week randomized controlled trial, the physical and mental health indexes of female students in Baduanjin group and control group were evaluated, including physical shape, physical function, physical fitness and scl-90 self-assessment scale.

RESULTS

Compared with the control group, at the end of the 12 week intervention, the weight, body mass index, diastolic blood pressure, systolic blood pressure, somatization score, obsessive-compulsive disorder score, interpersonal sensitivity score, depression score, anxiety score and phobic anxiety score of Baduanjin group decreased significantly. Compared with the control group, the vital capacity, vital capacity index, Stand test, Genchi test, Stand on 1 leg with closed eyes, standing bends and squats of the Baduanjin group have been significantly improved.

CONCLUSION

Compared with usual activities, Qigong Baduanjin exercise has advantages in improving female college students body shape (weight and body mass index), cardiovascular and respiratory lung function, flexibility, balance ability, muscle endurance and mental health.

C-DTW for Human Action Recognition Based on Nanogenerator

Sensor-based human action recognition (HAR) is considered to have broad practical prospects. It applies to wearable devices to collect plantar pressure or acceleration information at human joints during human actions, thereby identifying human motion patterns. Existing related works have mainly focused on improving recognition accuracy, and have rarely considered energy-efficient management of portable HAR systems. Considering the high sensitivity and energy harvesting ability of triboelectric nanogenerators (TENGs), in this research a TENG which achieved output performance of 9.98 mW/cm2 was fabricated using polydimethylsiloxane and carbon nanotube film for sensor-based HAR as a wearable sensor. Considering real-time identification, data are acquired using a sliding window approach. However, the classification accuracy is challenged by quasi-periodic characteristics of the intercepted sequence. To solve this problem, compensatory dynamic time warping (C-DTW) is proposed, which adjusts the DTW result based on the proportion of points separated by small distances under DTW alignment. Our simulation results show that the classification accuracy of C-DTW is higher than that of DTW and its improved versions (e.g., WDTW, DDTW and softDTW), with almost the same complexity. Moreover, C-DTW is much faster than shapeDTW under the same classification accuracy. Without loss of generality, the performance of the existing DTW versions can be enhanced using the compensatory mechanism of C-DTW.

Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree

Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.