An Efficient Approach to Sports Rehabilitation and Outcome Prediction Using RNN-LSTM

Cui, Yanli

doi:10.1007/s11036-024-02355-3

Mobile Netw Appl

2024

DOI: 10.1007/s11036-024-02355-3

|View full text |Cite

An Efficient Approach to Sports Rehabilitation and Outcome Prediction Using RNN-LSTM

Yanli Cui

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

The Impact of Machine Learning in Sport Injury Rehabilitation: A Specialist Perspective

Ahmed Kamel,

Ragheb Atallah

2024

International Journal of Innovative Science and Research Techno

View full text Add to dashboard Cite

One specific component of the athletic performance management paradigm is sports injuries and their rehabilitation. It plays a major role in a competitor's good recuperation and long-term physical well-being. This study looks at athletes' histories of sports recovery and develops several machine learning models based on these findings. This paper aims to assess the current state of machine learning applications for sports injuries and determine how each injury element— extrinsic, intrinsic, and triggering events—should be analyzed. The current dearth of models and open-source data sets, as well as the effectiveness of ML in sports injury prediction, are the conclusions drawn.

show abstract

The Impact of Machine Learning in Sport Injury Rehabilitation: A Specialist Perspective

Ahmed Kamel,

Ragheb Atallah

2024

International Journal of Innovative Science and Research Techno

View full text Add to dashboard Cite

show abstract

Advances in Wearable Sensor-Based Machine Learning for Mental Stress Detection: Techniques, Challenges, and Future Directions

2023

jdaai

View full text Add to dashboard Cite

Mental stress is a prevalent health issue that substantially impacts productivity, quality of life, and general well-being. Real-time stress detection and management have become possible because of the rapid advancements in machine learning and wearable sensor technology. This paper explores these emerging technologies and their application to mental stress detection, providing insights into the underlying factors influencing stress responses. We examine stress's physiological and psychological factors, highlighting critical biomarkers like heart rate variability (HRV) and electrodermal activity (EDA), which can be reliably captured through wearable sensors like ECG and PPG. Our analysis covers the essential detailing of the capabilities of various wearable sensors, data transfer and signal processing technologies, and data handling techniques that transform raw signals into meaningful stress indicators. Additionally, we delve into ML approaches for stress detection, comparing traditional algorithms with advanced models capable of recognizing complicated stress patterns from multimodal data. Furthermore, we address key challenges such as sensor quality, data diversity, and individual health variability that influence the robustness and accuracy of stress monitoring systems. This work underscores the potential of wearable sensor data and ML to present precise, proactive stress management solutions that could transform mental health monitoring and enhance intervention strategies.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

An Efficient Approach to Sports Rehabilitation and Outcome Prediction Using RNN-LSTM

Cited by 2 publications

References 32 publications

The Impact of Machine Learning in Sport Injury Rehabilitation: A Specialist Perspective

The Impact of Machine Learning in Sport Injury Rehabilitation: A Specialist Perspective

Advances in Wearable Sensor-Based Machine Learning for Mental Stress Detection: Techniques, Challenges, and Future Directions

Contact Info

Product

Resources

About