Abstract: A movement adjustment system based on heart rates and rating of perceived exertion (RPE) feedbacks of different users is provided, including: a user client, a heart rate monitoring device, a server, and a doctor client. The user client includes an RPE self-rating module, an RPE data acquisition module, a user-side video movement display module, and a user-side data storage module. The heart rate monitoring device includes an exercise data acquisition module. The server includes a data input module, a core processor, and a video movement library. The doctor client includes a doctor-side video movement display module, a video movement sending module, and a doctor-side data storage module. The above system according to the present disclosure adjusts, based on objective heart rate feedbacks during exercise and subjective RPE feedbacks after the exercise of different users, movements of next exercise of a patient.

Abstract: A movement adjustment system based on heart rates and rating of perceived exertion (RPE) feedbacks of different users is provided, including: a user client, a heart rate monitoring device, a server, and a doctor client. The user client includes an RPE self-rating module, an RPE data acquisition module, a user-side video movement display module, and a user-side data storage module. The heart rate monitoring device includes an exercise data acquisition module. The server includes a data input module, a core processor, and a video movement library. The doctor client includes a doctor-side video movement display module, a video movement sending module, and a doctor-side data storage module. The above system according to the present disclosure adjusts, based on objective heart rate feedbacks during exercise and subjective RPE feedbacks after the exercise of different users, movements of next exercise of a patient.

Abstract: The present disclosure relates to an apparatus and method for automatically adjusting an exercise prescription based on an exercise heart rate. The apparatus includes: a heart rate detection device, configured to detect a resting heart rate and a user's real-time exercise heart rate; an exercise movement library, configured to store various exercise instruction video and audio, and provide movement guidance for the user; an exercise guidance module, configured to retrieve and display movement video and audio from the exercise movement library to the user; and a central processing unit (CPU), configured to acquire the resting heart rate and the user's real-time exercise heart rate, calculate an effective exercise target heart rate range based on the resting heart rate, determine whether the real-time exercise heart rate falls within the effective exercise target heart rate range.

Abstract: Apersonalized exercise guidance system and method based on machine learning are provided. The personalized exercise guidance system includes: a server, a doctor client, and a patient client. The patient client is configured to obtain basic data, evaluation data, and exercise behavior data of a patient, and send the basic data, the evaluation data, and the exercise behavior data to the server. The doctor client is configured to determine an exercise prescription according to the basic data and medical record data of the patient, and upload the exercise prescription and the medical record data to the server. The server is configured to determine an exercise effect according the evaluation data and the exercise behavior data of the patient.

Abstract: The present disclosure relates to an apparatus and method for automatically adjusting an exercise prescription based on an exercise heart rate. The apparatus includes: a heart rate detection device, configured to detect a resting heart rate and a user's real-time exercise heart rate; an exercise movement library, configured to store various exercise instruction video and audio, and provide movement guidance for the user; an exercise guidance module, configured to retrieve and display movement video and audio from the exercise movement library to the user; and a central processing unit (CPU), configured to acquire the resting heart rate and the user's real-time exercise heart rate, calculate an effective exercise target heart rate range based on the resting heart rate, determine whether the real-time exercise heart rate falls within the effective exercise target heart rate range.