Scholarlink

<p>This article introduces the KneE-PAD, which is a dataset consisting of knee rehabilitation exercises performed by 31 patients suffering from knee pathologies. In particular, a total of 267 patients were monitored over a 6-month period where they were asked to perform in physiotherapy centers without any supervision 3 common lower limb rehabilitation exercises (squats, leg extension and walking). At each participant a set of 8 sEMG and IMU sensors were placed at important lower limb muscle groups. After curating and grouping the wrongly executed exercises, 2 common wrong variations for each exercise were identified in 31 participants, while a total of 2,086 files are available with each one having an approximate duration of 4.2s. The goal of KneE-PAD is to be used for training machine learning algorithms for automatic postural assessment using only wearable sensors (sEMG and IMU), which could become a vital part of a virtual coach to supervise the patients and provide useful feedback to them while executing their prescribed rehabilitation exercises remotely.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Knee Rehabilitation Exercises Dataset for Postural Assessment using Wearable Devices

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月11日

查看全文
  • Panagiotis Kasnesis,
  • Theodora Plavoukou,
  • Amalia Contiero Syropoulou,
  • Lazaros Toumanidis,
  • George Georgoudis

摘要

This article introduces the KneE-PAD, which is a dataset consisting of knee rehabilitation exercises performed by 31 patients suffering from knee pathologies. In particular, a total of 267 patients were monitored over a 6-month period where they were asked to perform in physiotherapy centers without any supervision 3 common lower limb rehabilitation exercises (squats, leg extension and walking). At each participant a set of 8 sEMG and IMU sensors were placed at important lower limb muscle groups. After curating and grouping the wrongly executed exercises, 2 common wrong variations for each exercise were identified in 31 participants, while a total of 2,086 files are available with each one having an approximate duration of 4.2s. The goal of KneE-PAD is to be used for training machine learning algorithms for automatic postural assessment using only wearable sensors (sEMG and IMU), which could become a vital part of a virtual coach to supervise the patients and provide useful feedback to them while executing their prescribed rehabilitation exercises remotely.