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Related Experiment Video

Updated: May 14, 2025

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

A Knee Rehabilitation Exercises Dataset for Postural Assessment using Wearable Devices.

Panagiotis Kasnesis¹, Theodora Plavoukou^2,3, Amalia Contiero Syropoulou²

¹ThinGenious PC, Marousi, 15125, Greece. pkasnesis@thingenious.io.

Scientific Data

|April 11, 2025

Summary

This study introduces the KneE-PAD dataset for knee rehabilitation exercises. It enables machine learning for remote patient monitoring and feedback using wearable sensors.

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Last Updated: May 14, 2025

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

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Published on: August 30, 2016

The Lower Body Positive Pressure Treadmill for Knee Osteoarthritis Rehabilitation

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Movement Retraining using Real-time Feedback of Performance

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Published on: January 17, 2013

Area of Science:

Biomedical Engineering
Rehabilitation Science
Wearable Technology

Background:

Knee pathologies require effective rehabilitation exercises.
Supervised physiotherapy can be resource-intensive and inaccessible.
Remote monitoring solutions are needed to support patient recovery.

Purpose of the Study:

To introduce the KneE-PAD dataset for knee rehabilitation.
To facilitate the development of machine learning models for automatic postural assessment.
To support the creation of virtual coaches for remote patient supervision.

Main Methods:

Collected data from 31 patients with knee pathologies performing 3 exercises (squats, leg extension, walking).
Utilized 8 sEMG and IMU sensors on lower limb muscle groups.
Identified common incorrect exercise variations from 2,086 recorded sessions.

Main Results:

The KneE-PAD dataset comprises 2,086 files of knee rehabilitation exercises.
Identified 2 common erroneous variations for each of the 3 exercises.
Data collected over a 6-month period from 267 monitored patients.

Conclusions:

KneE-PAD dataset is valuable for training AI in automatic postural assessment.
Wearable sensors (sEMG, IMU) can enable remote rehabilitation monitoring.
This technology can enhance virtual coaching for improved patient outcomes.