Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors

Laitrakun, Seksan

doi:10.3390/app13042475

Cited by 2 publications

(1 citation statement)

References 46 publications

(94 reference statements)

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“…Even with compressed datasets, the accuracy remained impressive at 94.18%. Another innovative approach is the merging-squeeze-excitation (MSE) technique for HAR using wearable sensors [23]. This method recalibrates feature maps during fusion, allowing the model to emphasise or suppress certain features based on their relevance.…”

Section: Centralised Learning-based Har Systemsmentioning

confidence: 99%

A Privacy and Energy-Aware Federated Framework for Human Activity Recognition

Khan,

Manzoor,

Ayaz

et al. 2023

Sensors

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Human activity recognition (HAR) using wearable sensors enables continuous monitoring for healthcare applications. However, the conventional centralised training of deep learning models on sensor data poses challenges related to privacy, communication costs, and on-device efficiency. This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) networks for energy-efficient and privacy-preserving HAR. The hybrid spiking-LSTM (S-LSTM) model synergistically combines the event-driven efficiency of SNNs and the sequence modelling capability of LSTMs. The model is trained using surrogate gradient learning and backpropagation through time, enabling fully supervised end-to-end learning. Extensive evaluations of two public datasets demonstrate that the proposed approach outperforms LSTM, CNN, and S-CNN models in accuracy and energy efficiency. For instance, the proposed S-LSTM achieved an accuracy of 97.36% and 89.69% for indoor and outdoor scenarios, respectively. Furthermore, the results also showed a significant improvement in energy efficiency of 32.30%, compared to simple LSTM. Additionally, we highlight the significance of personalisation in HAR, where fine-tuning with local data enhances model accuracy by up to 9% for individual users.

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Section: Centralised Learning-based Har Systemsmentioning

confidence: 99%

A Privacy and Energy-Aware Federated Framework for Human Activity Recognition

Khan,

Manzoor,

Ayaz

et al. 2023

Sensors

View full text Add to dashboard Cite

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A Privacy and Energy-Aware Federated Framework for Human Activity Recognition

Khan,

Manzoor,

Ayaz

et al. 2023

Preprint

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Human activity recognition (HAR) using wearable sensors is gaining popularity in the healthcare domain, enabling continuous monitoring and timely interventions. The conventional HAR systems are centralised and need data sharing with a server for processing and model training. However, this data sharing leads to various challenges, such as privacy concerns, increased communication and storage costs, inefficient computational algorithms, data scarcity due to unwillingness to share, and difficulty achieving high accuracy and personalised models simultaneously. To overcome these challenges, this paper introduces a privacy and energy-aware federated learning (FL) framework for model training without data sharing. The proposed scheme incorporates spiking neural networks (SNNs), which are particularly suited for resource-constrained devices due to their event-driven information processing. The key idea is to synergise the strengths of SNNs and long short-term memory (LSTM) to create a hybrid model for robust HAR. The proposed hybrid spiking LSTM (S-LSTM) model uses the LSTM as an input layer to capture temporal dependencies efficiently, followed by spiking layers that replace the typical activation using leaky integrate-and-fire neurons. We employ the combination of surrogate gradient learning and backpropagation through time (BPTT) to update the weights of SNN layers in a supervised manner. The performance of the proposed model is evaluated using two publicly available datasets for both indoor and outdoor scenarios. The results show that the proposed S-LSTM performs much better than spike convolutional neural networks (S-CNN) and simple CNN, achieving the accuracy of 97% and 87% for indoor and outdoor datasets, respectively. Personalisation, also known as fine-tuning the global model using local data, is crucial in HAR. It allows the model to adapt to individual behaviour and preferences, making predictions more accurate. The results also show that personalisation improves the performance of participants up to 9% on average. Additionally, analysis with 50% random client participation reduces communication costs by 50% with minimal impact on accuracy.

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Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors

Cited by 2 publications

References 46 publications

A Privacy and Energy-Aware Federated Framework for Human Activity Recognition

A Privacy and Energy-Aware Federated Framework for Human Activity Recognition

A Privacy and Energy-Aware Federated Framework for Human Activity Recognition

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