예방 관리와 기타 의료 활동을 지속해서 수행함으로써 긍정적인 효과를 얻을 수는 있지만 매일 같은 활동을 수행하면 정신 부담을 유발할 수 있습니다. 따라서 사용자가 스스로 할 수 있는 게임을 도입하여 운동하고자 하는 의욕을 유지하도록 도와주고 운동 시스템을 사용하도록 권장하는 연구가 수행되었습니다. 마이크로소프트(MS)는 사람의 자세와 관절의 3차원 좌표를 인식할 수 있는 키넥트(Kinect) 시스템은 개발하였고 재활 목적으로 손과 발의 움직임을 측정하기 위하여 키넥트(Kinect)를 사용하는 시스템을 연구하였습니다. 키넥트(Kinect)는 실제 사람의 자세를 감지할 수 있기 때문에 대항하는 운동을 인식하는 데도 사용될 수 있습니다. 최근 몇몇 키넥트(Kinect)기반 상업 재활 운동 시스템이 개발되었습니다. 이에 앞서 우리는 심도 이미지 센서를 사용한 프로토타입 의자 연습 지원 시스템을 설계하고 개발하였으며 시스템의 성능과 가용성을 검증하였습니다. 이 시스템은 키넥트(Kinect) 센서에서 얻은 골격과 RGB 데이터에 대한 3D 위치 데이터와 관절 각도를 기반으로 운동을 인식하고 평가합니다. 본 연구에서는 심도 이미지 센서를 이용하여 대항 운동을 지원하는 시스템을 설계하고 구현하여 평가하였습니다. 이 시스템은 심도 이미지 센서에서 얻은 사용자 관절에 관한 골격 데이터를 바탕으로 사용자의 운동을 인식하여 이를 평가하고 실시간 피드백을 제공합니다. 그리고 시청각 디스플레이를 사용하여 사용자에게 운동 순서를 설명하고 운동을 독려하기 위해 실시간 비디오를 재생합니다. 또한 리듬 게임 기능이 있어 음악과 함께 언제든지 운동할 수 있으며 상지/하지 대항 운동과 상지 좌우 대항 운동과 양팔과 다리를 이용한 가위바위보 운동과 이중/삼중 운동을 포함합니다.
Although positive effects are achieved by continuously performing preventive care and other health activities, performing the same activities every day can be mental strain. Therefore, research has been performed on maintaining the motivation and encouraging them to use exercise systems by incorporating games where the users perform activities. On the other hand, the Kinect system developed by Microsoft is able to recognize people’s postures and the three-dimensional coordinates of their joints, and work has been done to research and develop systems that use a Kinect to measure hand and foot movements for rehabilitation purposes. Since the Kinect can detect real-world human postures, it can also be used to recognize antagonistic exercises. Recently, several Kinect-based commercial rehabilitation systems have been developed. Formerly, we designed and developed a prototype lower-limb chair exercise support system using a depth image sensor and evaluated the performance and usability. The system recognizes and evaluates exercises based on 3D position data and joint angles for skeleton and RGB data obtained from the Kinect sensor. In this study, we designed, implemented and evaluated a system that supports antagonistic exercise using a depth sensor. It recognizes exercises by using skeleton data about the user’s joints acquired from a depth sensor, and evaluates the user’s exercises to provide real-time feedback. This system uses an audiovisual display to explain the exercise procedures to the user, and displays user’s real time video to encourage the user to perform the exercises. It also has a rhythm game function whereby the user can exercise in time with music. This system is provided with four types of exercise: upper/lower limb antagonistic movement, upper limb left/right antagonistic movement, rock/paper/scissors using both arms and both legs, and duple/triple time exercises.
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Continuously performing preventive care and other health care activities can have a positive impact; however performing the same activities daily can also result in mental strain. Therefore, research have been conducted on ways to consistently motivate users and encourage them to use exercise systems by incorporating games in which the users can perform voluntary activities. The Kinect system developed by Microsoft can recognize a person’s posture and the three-dimensional coordinates of their joints. Research and development of systems that use Kinect to measure hand and foot movements for rehabilitative purposes have been conducted. Kinect can detect real-world human postures therefore; it can also be used to recognize antagonistic exercises. Several Kinect-based commercial rehabilitation systems have recently been developed. Previously, we designed and developed a prototype lower-limb chair exercise support system that uses a depth sensor and evaluated its performance and usability. The system recognizes and evaluates exercises based on 3D position data and joint angles for skeletal and red-green-blue (RGB) data obtained from the Kinect sensor. In the present study, we designed, implemented, and evaluated a system that supports antagonistic exercises using a depth sensor. The system recognizes exercises by using skeletal data on the user’s joints acquired from a depth sensor, and evaluates the user’s exercises to provide real-time feedback. In addition, it uses an audiovisual display to explain the exercise procedures to the user and plays their real-time video to encourage users to exercise. It also has a rhythm game function whereby the user can exercise in-sync with music. Four types of exercises are included with this system: upper/lower limb antagonistic movement, upper limb left/right antagonistic movement, rock/paper/scissors using both arms and both legs, and duple/triple time exercises.
