KR-20260064901-A - TRAINING SERVICE SYSTEM FOR GENERATIVE AI BASED BICYCLE EXCERCISE AND TRAINING METHOD THEREBY

Abstract

The present invention provides a generative AI-based bicycle exercise training service system and a training method by the system, comprising a bicycle equipped with a wearable device and a sensor unit, a user terminal, and an artificial intelligence server which is a cloud-based AI trainer server, which collects and analyzes the user's heart rate, cadence, pedal rotation speed, deep breathing sounds, etc., in real time to generate and provide an optimal exercise prescription tailored to the individual's fitness level and goals, and can maximize exercise effects by utilizing generative AI to interact with the user and provide real-time feedback.

Inventors

• 양민철

Assignees

• 주식회사 에이아이씨랩

Dates

Publication Date

20260508

Application Date

20241030

Claims (5)

1. AI server; Indoor bicycle including a sensor unit; A wearable device that measures the user's biometric status during cycling exercise; A user terminal connected to the sensor unit and the wearable device via wireless communication to collect exercise and biometric data in real time and transmit the collected data to the artificial intelligence server; A DB that stores user information, accumulated user exercise data and biometric data and personalized exercise programs for each user; including, The above artificial intelligence server analyzes data received from the sensor unit or the user terminal to evaluate the user's physical condition, generates a personalized exercise program in the DB or updates the exercise program, and transmits it to the user terminal, forming a generative AI-based bicycle exercise training service system.
2. In paragraph 1, A generative AI-based bicycle exercise training service system characterized by the artificial intelligence server transmitting an exercise intensity adjustment notification or a rest notification to the user terminal in real time according to the analysis result of data received from the sensor unit or the user terminal.
3. In paragraph 1, A generative AI-based bicycle exercise training service system characterized in that the indoor bicycle includes an exercise load adjustment unit that operates according to instructions from the artificial intelligence server or the user terminal.
4. In paragraph 1, The above user terminal includes a camera that captures the user's facial complexion to collect image data and a microphone that records the user's breathing to collect voice data, and transmits the image data and the voice data to the artificial intelligence server. A generative AI-based bicycle exercise training service system characterized by the artificial intelligence server reflecting the video data and voice data when analyzing the user's physical condition.
5. A step of collecting the user's biometric data on a wearable device, and collecting the user's exercise data and biometric data by the sensor unit of an indoor bicycle; A step of receiving data from a wearable device and a sensor unit via wireless communication to a user terminal, transmitting it to an artificial intelligence server, and storing it in a DB; and A training method by a generative AI-based bicycle exercise training service system comprising the step of: the artificial intelligence server analyzing data received from the user terminal to evaluate the user's physical fitness and exercise ability, generating a personalized exercise program in a DB or updating the exercise program, and transmitting it to the user terminal.

Description

Generative AI-based Bicycle Exercise Training Service System and Training Method by the System The present invention relates to a generative AI-based bicycle exercise training service system and method used in the field of fitness and exercise training, and more specifically, to a generative artificial intelligence (AI)-based bicycle exercise training service system and a training method using the system that measures and analyzes a user's physical ability in real time during indoor bicycle exercise to provide a personalized exercise prescription. In modern society, people's physical activity is becoming increasingly insufficient, while their nutritional intake is increasing, leading to a continuous upward trend in obesity rates. Suitable exercises to address these problems include aerobic exercises such as jogging or cycling. However, outdoor exercise is easily affected by external factors such as weather or the environment, and there are no suitable places to engage in such activities in urban areas, making it difficult to exercise consistently. As a result, indoor cycling is emerging as an alternative. Although cycling is a simple exercise, maximizing its effectiveness requires adjusting the intensity appropriately based on the user's biometric data and fitness level. If one continues to exercise without considering their physical condition, not only will the workout fail to yield proper results, but there is also a risk of injury due to overexertion. This problem is particularly exacerbated in the case of home training performed alone. Additionally, while one can receive guidance from a trainer at an indoor training center, human trainers have limitations in checking or notifying the user's condition in real time. Consequently, they are forced to make subjective judgments on the user's state during each workout and determine the exercise intensity and program based on these assessments. Furthermore, human trainers have clear limitations in providing long-term exercise guidance because they cannot analyze a user's exercise results over the long term, resulting in instruction being limited to one-off sessions; additionally, it is practically difficult to continuously monitor the user's condition and provide appropriate feedback. Meanwhile, some equipment capable of measuring exercise volume has been disclosed among conventional indoor bicycles, but it has been limited mainly to measuring basic biometric data such as pedal rotation speed (cadence), speed, and heart rate. Data measured in this way not only has limitations in accurately assessing a user's physical fitness or exercise ability, but it is also difficult to derive an optimized exercise prescription based on such data. FIG. 1 is a block diagram illustrating a generative AI-based bicycle exercise training service system according to one embodiment of the present invention. FIG. 2 is a block diagram illustrating a training method by a generative AI-based bicycle exercise training service system according to one embodiment of the present invention. Hereinafter, preferred embodiments of the present invention will be described with reference to the attached drawings. However, embodiments of the present invention may be modified in various different forms, and the scope of the present invention is not limited to the embodiments described below. Furthermore, in describing the present invention, if it is determined that a detailed description of known technology related to the present invention may unnecessarily obscure the essence of the present invention, such detailed description is omitted. In addition, throughout the specification, the term 'includes' a component means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Generative AI-based bicycle exercise training service system The generative AI-based bicycle exercise training service system of the present invention installs a sensor unit on an indoor bicycle and wirelessly connects a user terminal and an artificial intelligence server, so that the artificial intelligence server generates a personalized exercise program suitable for the user based on exercise data and biometric data provided from the indoor bicycle and the user terminal, thereby enabling the user to improve training effects. The user rides an indoor bicycle and launches a dedicated app installed on the user's device. When the user starts exercising, the app connects the wearable device and the sensor unit, such as a cadence sensor mounted on the indoor bicycle, via wireless communication such as Bluetooth or short-range wireless communication to collect exercise data and biometric data in real time. Additionally, the app connects to a cloud-based AI server to transmit exercise and biometric data. At this time, the AI server analyzes the received data to evaluate the user's physical condition and generates a personalized exercise program. In addition, during exerci