# Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition
## 2 Related Work  
### Radar-Based HAR  
Radar-based HAR has evolved significantly, transitioning from basic motion detection to sophisticated machine learning-driven classification. Initially focused on simple movements, like walking and running, early studies utilized micro-Doppler spectrograms with CNNs for effective classification [10, 11]. As the field advanced, researchers like [12] enhanced activity recognition with high-resolution spectrograms, aiming to tap into the full potential of the radar by extracting more diverse movement features. Another study [13] employed micro-Doppler radar data combined with a dual-stream RNN architecture, achieving high accuracy in recognizing human activities, demonstrating significant potential for real-world IoT applications  
Recent innovations have embraced multi-representation fusion--integrating micro-Doppler, range-Doppler, and range-time data to enrich analysis--like [14, 15, 16] who fused micro-Doppler and range-time. This approach has proven effective in complex scenarios, including healthcare and smart home applications, as demonstrated by [17]. The cutting-edge research now explores three-dimensional radar data representations to cap ture intricate motion patterns, with [18] employing complex-valued neural networks for raw data processing, and [7] proposing hybrid networks for robust multi-domain fusion.  
A significant shift in the paradigm is the move towards continuous HAR, analyzing unbounded data streams to better reflect real-world conditions. Pioneering this approach, the authors in [19] introduced an open-source dataset for sequential human activities, employing techniques like SVM and later LSTM networks to handle continuous data [8; 20]. This transition from analyzing discrete activities to interpreting continuous human motions has set the stage for real-time, continuous activity recognition in dynamic environments, utilizing advanced signal processing and adaptive learning techniques [21; 22].  
Expanding beyond gross-motor activities, the authors in [23] applied Bi-LSTM networks to sequential ASL sign recognition, fusing range-Doppler and micro-Doppler data to bridge human-computer interaction gaps. Collectively, these works demonstrate radar's versatility in handling temporal complexity and fine-grained motion patterns, paving the way for robust, real-world HAR systems.  
One of the recent studies utilized distributed radar networks [24], employing a hybrid CNN-RNN classifier for spatio-temporal features extraction from multiple radars, achieving impressive accuracy on dynamic human activities. Building on this, the authors in [25] enhanced multi-node classification with recurrent networks that accurately capture ongoing human motions, considering both past and future movements.  
Another recent research study [26] utilized multi-domain fusion vision transformers that integrate data from range-time, range-Doppler, and Doppler-time maps, significantly enhancing accuracy and robustness. Meanwhile, in [27], they used a system that combines multi-domain data fusion with advanced signal processing techniques offers precise activity segmentation and classification, highlighting radar technology's capacity to overcome environmental and privacy issues. These developments promise substantial improvements in security and healthcare applications, showcasing the potential of HAR in practical scenarios.  
Together, these advancements reflect a significant leap toward practical, privacy-aware HAR systems capable of operating in complex environments like healthcare and smart homes, illustrating the benefits of combining distributed sensing with sophisticated machine learning techniques.