PDF(Pubmed)
Abstract：
BACKGROUND: Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual\'s functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity.
OBJECTIVE: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals.
METHODS: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition.
RESULTS: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs.
CONCLUSIONS: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization-being noninvasive and practically imperceptible to the user-which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.
OBJECTIVE: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals.
METHODS: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition.
RESULTS: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs.
CONCLUSIONS: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization-being noninvasive and practically imperceptible to the user-which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.
摘要：
背景：日常生活活动（ADL）对于独立和个人福祉至关重要，反映个人的功能状态。执行这些任务的障碍会限制自主性并对生活质量产生负面影响。ADL期间的身体功能评估对于运动限制的预防和康复至关重要。尽管如此，其传统的基于主观观察的评价在精确性和客观性方面存在局限性。
目的：本研究的主要目的是使用创新技术，特别是可穿戴惯性传感器结合人工智能技术，客观准确地评估人类在ADL中的表现。提出了通过实现允许在日常活动期间对运动进行动态和非侵入性监测的系统来克服传统方法的局限性。该方法旨在为早期发现功能障碍和个性化治疗和康复计划提供有效的工具，从而促进个人生活质量的提高。
方法：要监视运动，开发了可穿戴惯性传感器，其中包括加速度计和三轴陀螺仪。开发的传感器用于创建专有数据库，其中6个动作与肩膀有关，3个动作与背部有关。我们在数据库中注册了53,165个活动记录（包括加速度计和陀螺仪测量），在处理以删除null或异常值后，将其减少到52,600。最后,通过组合各种处理层创建了4个深度学习（DL）模型，以探索ADL识别中的不同方法。
结果：结果显示了4种提出的模型的高性能，有了准确的水平，精度,召回，所有类别的F1得分在95％至97％之间，平均损失0.10。这些结果表明，模型能够准确识别各种活动，在准确率和召回率之间取得了很好的平衡。卷积和双向方法都取得了稍微优越的结果，尽管双向模型在较少的时间内达到了收敛。
结论：实现的DL模型表现出了良好的性能，表明识别和分类与肩部和腰部区域相关的各种日常活动的有效能力。这些结果是通过最小的传感器实现的-是非侵入性的，并且实际上对用户来说是不可察觉的-这不会影响他们的日常工作，并促进对连续监测的接受和坚持。从而提高了收集数据的可靠性。这项研究可能对运动受限患者的临床评估和康复产生重大影响，通过提供客观和先进的工具来检测关键的运动模式和关节功能障碍。
目的：本研究的主要目的是使用创新技术，特别是可穿戴惯性传感器结合人工智能技术，客观准确地评估人类在ADL中的表现。提出了通过实现允许在日常活动期间对运动进行动态和非侵入性监测的系统来克服传统方法的局限性。该方法旨在为早期发现功能障碍和个性化治疗和康复计划提供有效的工具，从而促进个人生活质量的提高。
方法：要监视运动，开发了可穿戴惯性传感器，其中包括加速度计和三轴陀螺仪。开发的传感器用于创建专有数据库，其中6个动作与肩膀有关，3个动作与背部有关。我们在数据库中注册了53,165个活动记录（包括加速度计和陀螺仪测量），在处理以删除null或异常值后，将其减少到52,600。最后,通过组合各种处理层创建了4个深度学习（DL）模型，以探索ADL识别中的不同方法。
结果：结果显示了4种提出的模型的高性能，有了准确的水平，精度,召回，所有类别的F1得分在95％至97％之间，平均损失0.10。这些结果表明，模型能够准确识别各种活动，在准确率和召回率之间取得了很好的平衡。卷积和双向方法都取得了稍微优越的结果，尽管双向模型在较少的时间内达到了收敛。
结论：实现的DL模型表现出了良好的性能，表明识别和分类与肩部和腰部区域相关的各种日常活动的有效能力。这些结果是通过最小的传感器实现的-是非侵入性的，并且实际上对用户来说是不可察觉的-这不会影响他们的日常工作，并促进对连续监测的接受和坚持。从而提高了收集数据的可靠性。这项研究可能对运动受限患者的临床评估和康复产生重大影响，通过提供客观和先进的工具来检测关键的运动模式和关节功能障碍。
