A Hybrid Method Integrating A Musculoskeletal Model with Long Short-Term Memory (LSTM) for Human Motion Prediction.
A Hybrid Method Integrating A Musculoskeletal Model with Long Short-Term Memory (LSTM) for Human Motion Prediction.
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DOI：
10.1109/embc48229.2022.9871959
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发表时间：
2022-07-01
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影响因子：
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通讯作者：
Ding, Ziyun
中科院分区：
文献类型：
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作者：
Bian, Qingyao;Shepherd, Duncan Et;Ding, Ziyun
到目前为止，生物力学界对可穿戴技术及其临床应用的发展越来越感兴趣，这使得运动障碍的诊断和康复干预的设计成为可能。为了在高级康复设备的人机界面中提供可靠的反馈，开发了运动意图预测方法，旨在根据测量的运动生成未来的人体运动。惯性测量单元（IMU）是一种很有前途的运动跟踪装置，具有成本低、传感器放置方便等优点，几乎可以在任何环境中测量运动。然而，它揭示了用纯IMU数据进行人体运动预测的贡献很少。因此，我们提出了一种结合肌肉骨骼（MSK）模型和长短期记忆（LSTM）人工神经网络（ANN）的混合方法来预测人体运动。所提出的方法能够预测健康参与者在日常任务中的运动（站-坐-站和行走）：与IMU数据中测量的膝关节角度相比，预测的膝关节角度的RMSE为2.93°。该方法在运动预测方面优于未集成MSK模型（RMSE为31.26°）的基于ANN/MSK模型（RMSE为31.15°）和LSTM的方法。临床相关性-该模型仅基于IMU数据，具有巨大的潜力，可以成为一种低成本、易于使用的运动预测替代方案，在临床实践中与先进的康复设备相互作用。
So far, it shows a growing interest in the biomechanics community in the development of wearable technologies and their clinical applications, which enables the diagnosis of movement disorders and design of the rehabilitation interventions. To provide reliable feedback in the human-machine interface for advanced rehabilitation devices, methods to predict motion intention was developed which aim to generate future human motion based on the measured motion. An inertial measurement unit (IMU) is a promising device for motion tracking, with the advantages of low cost and high convenience in sensor placement to measure motion in almost every environment. However, it reveals that few contributions have been devoted to human motion prediction with pure IMU data. Thus, we propose a hybrid method integrating a musculoskeletal (MSK) model and the long short-term memory (LSTM) artificial neural network (ANN) to predict human motion. The proposed method was capable to predict motion in the daily tasks (stand-to-sit-to-stand and walking) for healthy participants: the predicted knee joint angles had an RMSE of 2.93° when compared to measured knee joint angles from the IMU data. The proposed method outperformed the methods based on the ANN/MSK model (RMSE of 31.15°) and LSTM without the integration of the MSK model (RMSE of 31.26°) in the motion prediction. Clinical Relevance- This proposed model based on IMU data alone has the great potential to become a low-cost, easy-to-use alternative in motion prediction to interact with advanced rehabilitation devices in clinical practice.