Al-enabled pulse rate based biofeedback device to guide fitness enthusiasts in performing exercises correctly

Title: AI-ENABLED PULSE RATE-BASED BIOFEEDBACK DEVICE TO GUIDE FITNESS ENTHUSIASTS IN PERFORMING EXERCISES CORRECTLY

Background: Exercise is an integral part of health and wellness. For optimal health benefits and injury prevention, individuals must understand their individual exercise thresholds (such as anaerobic threshold) in relation to various exercise intensities, including walking, running on a treadmill, and weight lifting. Self-analysis and control of exercise intensity help achieve fitness goals while preventing excessive strain on the body. However, it is challenging for individuals to closely monitor their exertion levels and maintain them within safe limits. A Pulse Rate-based AI-enabled Biofeedback device can address this need by providing real-time monitoring and feedback, thus preventing critical challenges to the physiological system.

Need of the study:

1. Monitoring Exertion Levels with Real-time Biofeedback.

2. Understanding Exercise Thresholds and performing submaximal exercises.

3. Identifying and understanding the progression of Exercises.

Research problem: The primary research problem addressed by this study is individuals’ difficulty in accurately monitoring their exertion levels and maintaining them within safe limits during exercise. Current fitness monitoring devices often lack real-time feedback and fail to provide personalized insights into exercise thresholds, resulting in submaximal workouts and increased injury risk. There is a need for an advanced, AI-enabled biofeedback device that offers real-time monitoring and feedback based on pulse rate, rhythm, and flow. This device will help users stay within their exercise thresholds, improve performance, reduce injury risk, and increase satisfaction compared to traditional self-monitoring methods.

Aim: This study aims to develop and validate an algorithm for  the rate, rhythm, and flow of the radial pulse which is taken by an AI-enabled pulse rate-based biofeedback device

Objectives:  

1. Evaluate the accuracy of an AI-enabled biofeedback device in predicting and maintaining exercise intensity within the optimal threshold, as measured by pulse rate, rhythm, and flow, compared to the actual Rate of Perceived Exertion (RPE).

2. Assess the impact of the AI-enabled biofeedback device on exercise efficiency and performance.

3. Determine the effectiveness of the AI-enabled biofeedback device in reducing injury risk during exercise.

**Methods:**Efficacy Testing  of AI-enable pulse rate-based biofeedback device: - (Two stages) This phase has two stages,

Stage 1: Exercise Data Analysis and Algorithm Development: Thirty healthy individuals will perform the Harvard Step Test at moderate intensity (RPE 13), with their exercise intensity, pulse rate, and step-up count recorded to develop an algorithm. Blood samples will be taken at moderate intensity to validate anaerobic threshold biomarkers.

Stage 2: Algorithm Evaluation and Accuracy Testing: The developed algorithm will be tested on a new set of 30 individuals to reach moderate intensity (RPE 13). The actual RPE will be recorded and correlated with the algorithm-predicted RPE to evaluate accuracy.