# Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition
## 1 Introduction  
Human activity recognition (HAR) has emerged as a cornerstone of modern intelligent systems, enabling applications in healthcare, smart homes, and sports analytics. In healthcare [1], HAR systems monitor patients' daily activities to detect early signs of cognitive decline or physical deterioration [2], facilitating timely interventions. In smart homes, HAR enhances user experience by adapting environments to residents' behaviors, such as adjusting lighting or temperature. Similarly, in sports analytics, HAR tracks athletes' movements to optimize performance and prevent injuries. HAR systems rely on diverse sensors [3, 4], including cameras, LiDAR, wearable devices, and radar, each with distinct trade-offs. Cameras provide rich visual data but raise privacy concerns and struggle with occlusions or poor lighting. Wearables like accelerometers capture precise motion data but are intrusive for long-term use. LiDAR offers spatial precision but remains cost-prohibitive for widespread deployment. Radar has recently emerged as a promising alternative, combining privacy preservation, robustness to lighting/occlusion, and continuous fine-grained motion capture. Early radar-based systems focused on human detection for security [5], but advances in radar technologies and signal processing have expanded applications to healthcare (e.g., vital sign monitoring) and smart homes (e.g., fall detection). Despite these advances, radar-based HAR remains challenging due to complex activity patterns. A critical ongoing challenge in HAR is accurately identifying continuous activities that mirror real-world human behavior [6]. Although some HAR methods, such as those based on RNNs or LSTMs [7, 8], can operate on continuous data streams without explicit segmentation, they are primarily effective only for single-activity classification and typically handle only time-dependent signals (e.g., time-range and spectrogram data) effectively. In contrast, radar data offer additional information, such as range-Doppler plots, which are independent of time but can provide details that the spectrogram cannot. To leverage the benefits of all these features and overcome these limitations, some researchers have advocated for multi-label classification as a strategy to approach the complexity of continuous HAR [9]. However, multi-label classification for continuous data inherently requires segmentation, which is indispensable for effectively handling the complexity of continuous HAR. Existing research has employed fixed window segmentation with overlap to manage continuous data streams by dividing them into manageable windows that capture one or multiple activities and the transitions between them. A significant hurdle remains in determining the optimal way to segment data to maximize the performance. Fixed window segmentation imposes arbitrary boundaries on continuous data streams, making it crucial to develop a more adaptive segmentation approach that better captures the natural flow of human activities and the transitions between them for multi-label HAR. To address these limitations, our paper proposes the following:  
* Feedback-driven segmentation framework: A system that dynamically optimizes segmentation parameters (e.g., window length, overlap) using Bayesian optimization (BO) and reinforcement learning (RL). This framework iteratively refines parameters based on performance metrics (accuracy, F1-score), adapting to variable activity durations and overlapping behaviors. * Multi-label ResNet18 architecture: A modified ResNet18 architecture tailored for multi-label classification, enhancing accuracy in complex scenarios. This adaptation effectively caters to the nuanced demands of radar-based human activity recognition. The rest of this paper is outlined as follows: Section 2 discusses the evolution of HAR from early techniques to continuous multi-label approaches and segmentation challenges. Section 3 outlines our methodology of our feedback-driven segmentation. Section 4 details the experimental setup and results.