Deep learning has been increasingly applied to improve human activity recognition (HAR) accuracy and reduce the human efforts of handcrafted feature extractions. Federated Learning (FL) is an emerging learning paradigm that enables the collaborative learning of a global model without exposing users' raw data. However, existing FL approaches yield unsatisfactory HAR performance as they fail to dynamically aggregate models according to the statistical diversity of users' data. In this paper, we propose FedDL, a novel federated learning system for HAR that can capture the underlying user relationships and apply them to learn personalized models for different users dynamically. Specifically, we design a dynamic layer sharing scheme that learns the similarity among users' model weights to form the sharing structure and merges models accordingly in an iterative, bottom-up layer-wise manner. FedDL merges local models based on the dynamic sharing scheme, significantly speeding up the convergence while maintaining high accuracy. We have implemented FedDL and evaluated using a new data set we collected using LiDAR and four public real-world datasets involving 178 users in total. The results show that FedDL outperforms several state-of-the-art FL paradigms in terms of model accuracy (by more than 15%), converging rate (by more than 70%), and communication overhead (about 30% reduction). Moreover, the testing results on the datasets of different scales show that FedDL has high scalability and hence can be deployed for large-scale real-world applications.

Nowadays, field-programmable gate array (FPGA) devices have been widely used in various fields. However, modules of circuits to be executed on FPGAs are placed within rectangular reconfigurable regions (RRs) with current floorplanners, leading to internal fragments, and lower utilization of resources. To address this, a dynamic description model of RRs and the corresponding floorplanner named dynamic union partial reconfiguration floorplan (DUPRFloor) are proposed in this article. The RR dynamic description modeling adds an anchor within a rectangular RR to reduce internal fragments. In this way, the modeling can represent both rectangular and nonrectangular shapes. Then, to find the optimal anchor, a clipping method is devised by constraining width and height of the candidate region. Finally, the mixed-integer linear programming (MILP) is used to optimize an objective function which considers the resources utilization and communication costs to obtain a desirable floorplanning result. The proposed method has been validated by simulation on three kinds of devices. And experimental results show that reconfigurable resources can be saved as much as 19.16% compared to rectangular modeling method. The DUPRFloor is also validated on the Microelectronics Center of North Carolina standard benchmark data sets. Results show that DUPRFloor can reduce 18.65% global wire length at most with almost the same execution time compared to state-of-the-art algorithms. Our approach is tested on a FPGA implemented software-defined radio (SDR) and reduced 29.41% wasted configurable frames, and to the overall design, 2% configurable frames are saved at most.

Heart rate (HR) estimation based on photoplethysmography (PPG) signals has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user’s physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios, making them impractical in real-world settings where a user may perform a wide range of physical activities. In this article, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from electrocardiogram signals based on a training data set. Then a set of denoising convolutional neural networks (DCNNs) are trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by an ensemble of DCNNs and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.61 beats per minute (bpm), outperforming a state-of-the-art deep learning approach (4 bpm) and a classical signal processing approach (2.34 bpm).

PPG-based heart rate estimation has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user's physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios (e.g., running or biking), making them impractical in real-world settings where a user may perform a wide range of physical activities. In this paper, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from ECG signals based on a training data set. Then a denoising convolutional neural network (DnCNN) is trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by the DnCNN and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup (SPC) training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.98 bpm, outperforming two state-of-the-art methods TROIKA and Deep PPG.

Tracking heart rate for fitness using wrist-type wearables is challenging, because of the significant noise caused by intensive wrist movements. In this paper, we present FitBeat - a lightweight system that enables accurate heart rate tracking on wrist-type wearables during intensive exercises. Unlike existing approaches that rely on computation- intensive signal processing, FitBeat integrates and augments standard filter and spectral analysis tool, which achieves comparable accuracy while significantly reducing computational overhead. FitBeat integrates contact sensing, motion sensing and simple spectral analysis algorithms to suppress various error sources. We implement FitBeat on a COTS smartwatch, and evaluate the performance of FitBeat for typical workouts of different intensities, including walking, running and riding. Experimental results involving 10 subjects show that the average error of FitBeat is around 4 beats per minute, which improves heart rate accuracy of the default heart rate tracker of Moto 360 by 10x.