Smartphones provide an attractive yet challenging platform for human activity recognition (HAR). They are ubiquitous, but also limit the input of HAR systems to a single IMU. These systems are also challenged by the inherent diversity of human activities and varying phone placement on the user’s body. This results in traditional smartphone HAR systems having limited personalization potential or imposing a high user burden. We propose ActivitySeeker, a personalized smartphone HAR system that combines self-supervised activity discovery and low-burden user interaction to collaboratively label IMU data and adapt HAR models to individual users on-device through transfer learning. We evaluated ActivitySeeker through simulated online learning and in-the-wild user experiments, where it discovered 95.5% of personal activity types and achieved high recognition accuracy (93.3%) while maintaining a positive user experience. Leveraging the synergy between user and smartphone, ActivitySeeker opens up new possibilities for HAR-based applications like fitness, health and personalized recommendation.

Social intelligence is vital for effective human-AI interaction. While LLMs demonstrate strong text-based social intelligence, the vision modality remains challenging due to the presence of non-verbal social cues. For example, gaze is the primary conveyor of social attention, yet it cannot be accurately perceived and understood by multimodal LLMs (MLLMs). Therefore, we propose GazeCoT, a pipeline using gaze estimation models to provide MLLMs with the attention of people in images or videos. The gaze information is provided as visual and text prompts compiled into a structured context to support MLLM social reasoning. Benchmark evaluation confirms that GazeCoT enhances MLLMs’ social intelligence by improving gaze perception. A user study in a challenging application involving parent-child interactions demonstrates that GazeCoT improves perceived explainability and trustworthiness by aligning MLLM social perception and social reasoning with human norms. We hope that GazeCoT, a versatile plug-and-play pipeline, can enable socially aware, MLLM-based HCI applications.

Long-range Human-Robot Interaction (HRI) remains underexplored. Within it, Command Source Identification (CSI) — determining who issued a command — is especially challenging due to multi-user and distance-induced sensor ambiguity. We introduce HiSync, an optical-inertial fusion framework that treats hand motion as binding cues by aligning robot-mounted camera optical flow with hand-worn IMU signals. We first elicit a user-defined (N=12) gesture set and collect a multimodal command gesture dataset (N=38) in long-range multi-user HRI scenarios. Next, HiSync extracts frequency-domain hand motion features from both camera and IMU data, and a learned CSINet denoises IMU readings, temporally aligns modalities, and performs distance-aware multi-window fusion to compute cross-modal similarity of subtle, natural gestures, enabling robust CSI. In three-person scenes up to 34 m, HiSync achieves 92.32% CSI accuracy, outperforming the prior SOTA by 48.44%. HiSync is also validated on real-robot deployment. By making CSI reliable and natural, HiSync provides a practical primitive and design guidance for public-space HRI.