The control of active prostheses and orthoses requires the precise classification of instantaneous human activity and the detection of specific events within each activity. Furthermore, such classification helps physiotherapists, orthopedists, and neurologists in kinetic/kinematic analyses of patients’ gaits. To address this need, we propose an innovative deep neural network (DNN)-based approach with a two-step hyperparameter optimization scheme for classifying human activity and gait events, specific for different motor activities, by using the ENABL3S dataset. The proposed architecture sets the baseline accuracy to 93% with a single hidden layer and offers further improvement by adding more layers; however, the corresponding number of input neurons remains a crucial hyperparameter. Our two-step hyperparameter-tuning strategy is employed which first searches for an appropriate number of hidden layers and then carefully modulates the number of neurons within these layers using 10-fold cross-validation. This multi-class classifier significantly outperforms prior machine learning algorithms for both activity and gait event recognition. Notably, our proposed scheme achieves impressive accuracy rates of 98.1% and 99.96% for human activity and gait events per activity, respectively, potentially leading to significant advancements in prosthetic/orthotic controls, patient care, and rehabilitation programs’ definition.

Tasmiyah Javed

Ali Raza

Hafiz Farhan Maqbool

Saqib Zafar

Juri Taborri

Stefano Rossi

Journal of Sensor and Actuator Networks

Journal of Sensor and Actuator Networks (ISSN 2224-2708) is an international open access journal on the science and technology of sensor and actuator networks. It publishes regular research papers, reviews (including comprehensive reviews on complete sensor and actuator networks), and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.

The Journal of Sensor and Actuator Networks is an international open access journal dedicated to the science and technology of sensor and actuator networks. It publishes regular research papers, reviews (including comprehensive reviews on complete sensor and actuator networks), and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There are no restrictions on the length of the papers, and full experimental details must be provided to ensure that the results can be reproduced.

《Journal of Sensor and Actuator Networks》是一本国际开放获取期刊，专注于传感器和执行器网络的科学与技术。期刊发表常规研究论文、综述（包括对完整传感器和执行器网络的综合评述）以及简短通讯。我们的目标是鼓励科学家尽可能详细地发布他们的实验和理论结果。对论文的长度没有限制，必须提供完整的实验细节，以便结果能够被复现。

Multi-Class Classification of Human Activity and Gait Events Using Heterogeneous Sensors

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