WiFi-based gesture recognition enables intuitive human-computer interaction but struggles with user diversity, where variations in body shape and behavior reduce generalization. Existing domain-invariant methods often discard user-dependent features, overlooking their potential for personalization. Domain-adaptation approaches rely on additional user data but lack mechanisms to mitigate the negative influence of user-dependent features. This paper introduces UserFi, a framework that disentangles user-independent and user-dependent features, reintegrates them for prediction, and applies a joint fine-tuning strategy in a federated setting to balance generality and specificity. Experiments show that UserFi effectively transforms user diversity from a limitation into a benefit for WiFi-based gesture recognition. It achieves 84.6% accuracy on unseen users, 8.3% higher than the representative baselines.

Wi-Fi technology is becoming a promising enabler of device-free fitness tracking to provide reviews and recommendations for effective homely exercise. State-of-the-art Wi-Fi fitness assistants succeed in recognizing the simple meta-movements (e.g., Push-Up and Squat) with discrete and repeatable patterns. Unfortunately, these prior attempts can hardly scale to the combination movements of ever-growing interests in intensive fitness programs. Combination movements are composed of meta-movements that are mutually concatenated or inserted. They have a compound characteristic that inherits from the diversity of combination orders and continuity of meta-movements. The compound characteristic causes substantial training data collection costs and a challenge of combination decomposition that is a prerequisite for providing fine-grained fitness assessment. To this end, we proposeInFit, a Wi-Fi-based device-free fitness assistant system for combination movements. First, we design a novel data augmentation method, namelyStitching-based Virtual Sample Generation(SVSG), to reduce the training data collection costs by generating virtual combination movements. Second, a 2-stage combination movement recognition model is designed to learn temporal dependencies between movements and decompose combination movements. From its outputs, we can tell whether a combination movement is standard. Extensive experimental results show that InFit can achieve an average recognition accuracy of$94\%$. With zero training samples of combination movements, the average accuracy is$40\%$higher than the baselines. In addition, SVSG can provide a general enhancement on multiple competing schemes with similar sensing tasks.

Federated learning (FL) has been gaining popularity as a way to provide privacy-preserving data sharing for the Internet of Medical Things (IoMT). As a complementary, blockchain technology is used in recent literature to make FL secure. However, existing blockchain-based FL (BFL) solutions do not perform well when data in a BFL cluster are sparse. A direct solution is to collect as many devices as possible to establish a large BFL cluster. However, these devices may locate in geographically distant areas and be separated by great distance, which further results in high communication latency. The high latency will lead to BFL’s low system efficiency due to frequent communications in the blockchain consensus. In this article, we propose that the large cluster should be divided into multiple smaller clusters, each in its own geographical area and organized with a BFL. In this context, we propose CFL, a cross-cluster FL system facilitated by the cross-chain technique. CFL connects multiple BFL clusters, where only a few aggregated updates are transmitted over long distances across clusters, thus improving the system efficiency. The design of CFL focuses on a cross-chain consensus protocol, which guarantees the model updates to be exchanged securely across clusters. We carry out extensive experiments to evaluate CFL in comparison with BFL, and show both CFL’s feasibility and efficiency.

Effective finger writing underpins semantic text input task for virtual reality applications. We demonstrate WiWrite, a novel finger writing system that leverages the ubiquitous wireless signals to enable character recognition and word estimation. WiWrite decomposes characters into basic strokes and distinguishes them by exploring the unique signal reflection pattern caused by finger movements. Apart from the prior arts relying on specialized sensors or wearable devices, WiWrite is device-free without any accessories in deed. Our real-world experiments in two different scenarios show that WiWrite succeeds in classifying 26 upper-case alphabets with 90.8% average accuracy and recognizing simple words with an average accuracy of 96.7%.

Fingerprinting-based indoor localization via WiFi has achieved a great breakthrough in the past decade. However, it suffers from an inherent problem that the localization accuracy declines sharply over time due to the dynamic environment and unstable WiFi devices. Researchers have designed many methods to update the localization model, e.g., crowdsourcing-based model updating, in order to maintain the localization accuracy. Unfortunately, they have not taken the privacy into consideration during the updating process. This will lead to a threat that the eavesdroppers could guess the location providers' private information according to the updating model. For the goal of maintaining the localization accuracy without the risk of privacy breaching, we proposed FLoc, a fingerprinting-based indoor localization system which updates the localization model via a federated learning framework. In FLoc, every provider maintains a local localization model in their own device. They will regularly encrypt the updating parameters and share them to a common model server. At the model server, it aggregates the encrypted information of the local models to maintain a general model, which will be sent back to the local devices for next updating iteration. We evaluate FLoc in an APs unknown laboratory corridor. The experiment results show that FLoc has a comparable localization performance. Moreover, it can successfully protect the providers' privacy, since the information transferred is all encrypted.