Detección de similitudes y diferencias dentro de un mismo movimiento de golpeo mediante un análisis del rendimiento basado en inteligencia artificial: ejemplo del servicio en tenis

El análisis del rendimiento basado en inteligencia artificial (IA) tiene el potencial de apoyar la retroalimentación en el entrenamiento. Sin embargo, aún no se ha propuesto un método útil. El objetivo de este estudio es desarrollar un análisis del rendimiento basado en IA para apoyar el entrenamiento de tenis. En concreto, se investiga la precisión en la detección de similitudes y diferencias dentro de un mismo movimiento de golpeo. Los participantes fueron dos tenistas con más de diez años de experiencia en tenis a nivel regional. Este estudio se centró en el servicio en tenis y se grabaron videos de los dos primeros servicios desde ambos lados de la cancha (número de servicios: 40 intentos) con un teléfono inteligente situado en la valla detrás del participante. El código de análisis se ejecutó en Python, y la parte principal involucró el uso de BlazePose, que estima las coordenadas X, Y y Z de una posición humana. Se cortaron videos de 2 s, con un solapamiento de 1 s entre cada video, y se eligió manualmente uno de ellos como el video estándar. Los videos se compararon con los de comparación y se calcularon automáticamente las puntuaciones de diferencia para el total y para cada parte del cuerpo. Se realizó un análisis basado en IA que consideraba 12 condiciones y combinaba los dos primeros servicios desde ambos lados y de los diferentes jugadores. Como resultado, se confirmó cierta precisión (≥ 70%) en la detección de fases solapadas entre videos. Además, las partes del cuerpo evaluadas manualmente que mostraban movimientos diferentes por un entrenador certificado correspondían con las tres primeras partes diferentes del análisis basado en IA para 8 de las 12 condiciones. El análisis de rendimiento basado en IA propuesto puede extraer eficazmente fases similares o solapadas y sugerir partes del cuerpo que muestran movimientos diferentes.Artificial intelligence (AI) -based performance analysis has the potential to support feedback in coaching; however, a useful method has not yet been proposed. This study aims to develop an AI-based performance analysis to support tennis coaching. Specifically, we investigate the accuracy of detecting similarities and differences within the same shot movement. The participants were two tennis players with more than ten years of tennis experience at the regional level. This study targeted service in tennis and videos of the 1st and 2nd service from both sides (number of services: 40 attempts) were recorded using a smartphone located on the fence behind the participant. The analysis code was executed in Python, and the main part involved the use of BlazePose, which estimates the X-, Y-, and Z-coordinates of a human pose. Video clips of 2 s were cut, with a 1 s overlap between each clip, and one of the clips was manually chosen as the standard clip. The clips were compared with the comparison clips, and the difference scores for the total and each body part were automatically calculated. An AI-based analysis was conducted considering 12 conditions combining the 1st and 2nd services from both sides and different players. As a result, a certain accuracy (≥ 70%) was confirmed for detecting overlapping phases between clips. Moreover, manually evaluated body parts that showed different movements by a certified coach corresponded to the top three different parts in the AI-based analysis for 8 of the 12 conditions. The proposed AI-based performance analysis can effectively extract similar or overlapping phases and suggest body parts exhibiting different movements

Detección de similitudes y diferencias dentro de un mismo movimiento de golpeo mediante un análisis del rendimiento basado en inteligencia artificial: ejemplo del servicio en tenis

This research was supported in part by grants from the Advanced Research Initiative for Human High Performance (ARIHHP), University of Tsukuba [grant number 2022(I)6], and by a JSPS KAKENHI Grant [grant number 22K17747].Artificial intelligence (AI) -based performance analysis has the potential to support feedback in coaching; however, a useful method has not yet been proposed. This study aims to develop an AI-based performance analysis to support tennis coaching. Specifically, we investigate the accuracy of detecting similarities and differences within the same shot movement. The participants were two tennis players with more than ten years of tennis experience. This study targeted service in tennis and videos of the 1st and 2nd service from both sides were recorded using a smartphone located on the fence behind the participant. The analysis code was executed in Python, and the main part involved the use of BlazePose, which estimates the X-, Y-, and Z-coordinates of a human pose. Video clips of 2 s were cut, with a 1 s overlap between each clip, and one of the clips was manually chosen as the standard clip. The clips were compared with the comparison clips, and the difference scores for the total and each body part were automatically calculated. As a result, a certain accuracy (≥ 70%) was confirmed for detecting overlapping phases between clips. Moreover, manually evaluated body parts that showed different movements by a certified coach corresponded to the top three different parts in the AI-based analysis for 8 of the 12 conditions. Performance analysis provides feedback in tennis coaching.El análisis del rendimiento basado en inteligencia artificial (IA) tiene el potencial de apoyar la retroalimentación en el entrenamiento. Sin embargo, aún no se ha propuesto un método útil. El objetivo de este estudio es desarrollar un análisis del rendimiento basado en IA para apoyar el entrenamiento de tenis. En concreto, se investiga la precisión en la detección de similitudes y diferencias dentro de un mismo movimiento de golpeo. Los participantes fueron dos tenistas con más de diez años de experiencia en tenis a nivel regional. Este estudio se centró en el servicio en tenis y se grabaron videos de los dos primeros servicios desde ambos lados de la cancha (número de servicios: 40 intentos) con un teléfono inteligente situado en la valla detrás del participante. El código de análisis se ejecutó en Python, y la parte principal involucró el uso de BlazePose, que estima las coordenadas X, Y y Z de una posición humana. Se cortaron videos de 2 s, con un solapamiento de 1 s entre cada video, y se eligió manualmente uno de ellos como el video estándar. Los videos se compararon con los de comparación y se calcularon automáticamente las puntuaciones de diferencia para el total y para cada parte del cuerpo. Se realizó un análisis basado en IA que consideraba 12 condiciones y combinaba los dos primeros servicios desde ambos lados y de los diferentes jugadores. Como resultado, se confirmó cierta precisión (≥ 70%) en la detección de fases solapadas entre videos. Además, las partes del cuerpo evaluadas manualmente que mostraban movimientos diferentes por un entrenador certificado correspondían con las tres primeras partes diferentes del análisis basado en IA para 8 de las 12 condiciones. El análisis de rendimiento basado en IA propuesto puede extraer eficazmente fases similares o solapadas y sugerir partes del cuerpo que muestran movimientos diferentes.University of Tsukuba [2022(I)6]JSPS KAKENHI Grant [22K17747

Feasibility, Safety, Enjoyment, and System Usability of Web-Based Aerobic Dance Exercise Program in Older Adults: Single-Arm Pilot Study

BackgroundDuring the COVID-19 epidemic, opportunities for social interaction and physical activity among older people are decreasing, which may have a negative impact on their health. As a solution, a web-based group exercise program provided through a videoconferencing platform would be useful. As a web-based exercise program that older adults can easily, safely, and enjoyably perform at home, we developed a short-duration, light-intensity aerobic dance exercise program. Before studying the effectiveness of this exercise program, its characteristics, such as feasibility, safety, enjoyment, and system usability, should be examined among older adults. ObjectiveThis pilot study aimed to examine the feasibility, safety, and enjoyment of a web-based aerobic dance exercise program and the usability of a web-based exercise delivery system using a videoconferencing platform for older adults. MethodsThis study was designed as a prospective single-arm pilot study. A total of 16 older adults participated in an 8-week web-based aerobic dance program held every morning (8:30 AM to 8:50 AM) on weekdays at home. Retention and adherence rates were measured for the program’s feasibility. Safety was assessed by the heart rate reserve, an index of exercise intensity calculated from heart rate, and the number of adverse events during exercise sessions. Enjoyment of this exercise program was assessed by an 11-point Likert scale ranging from 0 (not enjoyable at all) to 10 (extremely enjoyable) obtained through telephone interviews after the first-, third-, sixth-, and eighth-week intervention. For usability, the ease of the videoconferencing platform system was assessed through telephone interviews after the intervention. ResultsA female participant with hypertension dropped out in the second week because of the continuously reported high blood pressure (≥180 mmHg) before attending the exercise session in the first week. Therefore, the retention rate was 93.8% (15/16). Among the remaining participants, the median (IQR) overall adherence rate was 97.4% (94.7-100). Regarding safety, the mean (SD) heart rate reserve during the aerobic dance exercise was 29.8% (6.8%), showing that the exercise was relatively safe with very light to light intensity. There were no adverse events during the exercise session. The enjoyment score (0-10 points) significantly increased from the first (6.7 [1.7]) to sixth (8.2 [1.3]) and eighth week (8.5 [1.3]). Regarding usability, 11 participants reported difficulties at the beginning, such as basic touch panel operations and the use of unfamiliar applications; however, all got accustomed to it and subsequently reported no difficulty. ConclusionsThis study showed high feasibility, enjoyment, and safety of the web-based aerobic dance exercise program in older adults, and the web-based exercise delivery system may have areas for improvement, albeit without serious problems. Our web-based aerobic dance exercise program may contribute to an increase in physical and social activities among older adults