Bath Natural Environment HAR Data Set

Sherratt, Freddie

doi:10.5281/zenodo.4390499

Cited by 1 publication

(1 citation statement)

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“…This study was conducted using a public human locomotion dataset ( Sherratt, 2020 ) collected by the authors’ group from able-bodied participants, where six walking modes were measured, comprising stair ascent/descent, ramp ascent/descent, stopped, level-ground walking and transition between modes were measured ( Sherratt et al, 2021 ). Five 9-axis inertial measurement unit (IMU) sensors consisted of accelerometer, gyroscope and magnetometer are placed in the body locations, two on the legs at the ankles (ankle for short), two on the torso at the hips (hip for short) and one on the chest.…”

Section: Methodsmentioning

confidence: 99%

Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities

Ding

Plummer²,

Georgilas³

2022

Front. Bioeng. Biotechnol.

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Reliable estimation of desired motion trajectories plays a crucial part in the continuous control of lower extremity assistance devices such as prostheses and orthoses. Moreover, reliable estimation methods are also required to predict hard-to-measure biomechanical quantities (e.g., joint contact moment/force) for use in sports injury science. Recognising that human locomotion is an inherently time-sequential and limb-synergetic behaviour, this study investigates models and learning algorithms for predicting the motion of a subject’s leg from the motion of complementary limbs. The novel deep learning model architectures proposed are based on the Long Short-Term Memory approach with the addition of an attention mechanism. A dataset comprising Inertial Measurement Unit signals from 21 subjects traversing varied terrains was used, including stair ascent/descent, ramp ascent/descent, stopped, level-ground walking and the transitions between these conditions. Fourier Analysis is deployed to evaluate the model robustness, in addition to assessing time-based prediction errors. The experiment on three unseen test participants suggests that the branched neural network structure is preferred to tackle the multioutput problem, and the inclusion of an attention mechanism demonstrates improved performance in terms of accuracy, robustness and network size. An experimental comparison found that 57% of the model parameters were not needed after adding attention layers meanwhile the prediction error is lower than the LSTM model without attention mechanism. The attention model has errors of 9.06% and 7.64% (normalised root mean square error) for ankle and hip acceleration prediction respectively. Also, less high-frequency noise is present in the attention model predictions. We conclude that the internal structure of the proposed deep learning model is justified, principally the benefit of using an attention mechanism. Experimental results for biomechanical motion estimation are obtained, showing greater accuracy than only with LSTM. The trained attention model can be used throughout despite transitioning between terrain types. Such a model will be useful in, for example, the control of lower-limb prostheses, instead of the need to identify and switch between different trajectory generators for different walking modes.

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Section: Methodsmentioning

confidence: 99%