“…Existing methods use manual feature selection and extraction techniques based on domain knowledge. To automate this process, a recent research study [25] presents a data-driven four-layer Deep Neural Network (DNN) that contains Convolution Neural Network (CNN) and Long and Short Term Memory (LSTM). The CNN automatically detects user-defined patterns in the data and uses as a feature extractor for PPG signal.…”
Health data is being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also presents opportunities within computer security, specifically w ith b iometric s ystems u sed f or i dentification an d authentication purposes. This paper performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work. CCS Concepts: • Security and privacy → Biometrics.
“…Existing methods use manual feature selection and extraction techniques based on domain knowledge. To automate this process, a recent research study [25] presents a data-driven four-layer Deep Neural Network (DNN) that contains Convolution Neural Network (CNN) and Long and Short Term Memory (LSTM). The CNN automatically detects user-defined patterns in the data and uses as a feature extractor for PPG signal.…”
Health data is being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also presents opportunities within computer security, specifically w ith b iometric s ystems u sed f or i dentification an d authentication purposes. This paper performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work. CCS Concepts: • Security and privacy → Biometrics.
“…4(a) represents the workflow of the proposed deep learning framework LoCoMo-Net, consisting the CNN model with a two-stage pipeline: input data compression and a data-driven weight sharing. The LoCoMo-Net framework was formulated as one versus all (binary) classification problem due to its feasibility for training initialization as well as usage in a practical scenario [30] . FIGURE 4.…”
Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement recognition-Net' (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets-our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin-Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT's), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and lowcomplex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.
“…Some of them relied on the signal fundamental characteristics to extract features [8] while others used non-fiducial features through wavelet transform [9]. Lately, researchers focused on learned methods from deep learning models [10,11]. The main drawback of the current systems is the impracticality in real life because of the low accuracy or the lack of robustness.…”
Section: Related Workmentioning
confidence: 99%
“…Later, fully connected layer is appended with sigmoid and binary cross entropy is used to give the classification result. The model was inspired and extended from [10,17] while the hyperparameters are tuned according to the performances in all used databases. In addition, L2 regularization and 10-fold cross validation were used to control overfitting.…”
This paper investigates the employment of photoplethysmography (PPG) for user authentication systems. Time-stable and user-specific features are developed by stretching the signal, designing a convolutional neural network and performing a variation-stable approach with three score fusions. Two evaluation scenarios are explored, namely single-session and twosessions. In the earlier, the training and testing are done solely on one session data to find the user-specific features, while the second scenario is performed on data from two different sessions to test the time permanence of the features. The verification system was tested on four databases achieving an accuracy of 100% for single-session and 87.3% for two-sessions cases. The simulation results confirm the effectiveness of proposed variation-stable fusion which can be extended to other biometrics. The code is available in [1].
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