## 4 Results and Discussion
### Movement Disorder Classification  
In the multi-stage movement disorder identification process, where the initial stage identifies a signal as anomalous, the signal segment proceeds to the second stage for multi-label classification, which determines the type or types of anomalies present in the input signal segment. Here, too, HARDenseRNN demonstrates superior recall, particularly with a segment size of 250 and S1 labeling, achieving the highest recall of 91.54%, along with an accuracy of 79.71% and precision of 91.95%. For S2 labeling, HARDenseRNN also performs well, with a recall of 90.54% at a segment size of 250 and 89.82% at a segment size of 150. However, HARDenseRNN exhibits greater variability in its performance, with recall dropping to 79.13% and 80.33% at smaller segment sizes of 50 for S2 and S1 labeling, respectively. Random forest shows consistent (across various pre-processing configurations) but lower recall compared to HARDenseRNN. The highest recall for random forest is 71.99% at a segment size of 150 with S2 labeling, followed closely by 71.23% at a segment size of 250 with S2 labeling. Recall values are generally lower with S1 labeling, with the best  
Figure 7: (**A**): Confusion matrix for movement disorder detection using HARDenseRNN with a segment size of 250 and S1 labeling. (**B–D**): Confusion matrix for movement disorder classification (multi-label classification) using HARDenseRNN with a segment size of 250 and S1 labeling.  
performance being 67.72% at a segment size of 250. Despite this, random forest maintains relatively high precision across all configurations, indicating its reliability in classification.  
Figure 8: Inference time for various tasks (feature engineering, movement disorder detection, movement disorder classification). (A) Inference time when using all 150 features (random forest-based pipeline), (B) inference time when using top 10 features (random forest-based pipeline), and (C) inference time when using HARDenseRNN. The best inference time of 165 ms is observed for the deep learning pipeline.  
Figure 9: Performance metrics (accuracy, precision, and recall) for 12 movement disorder detection models, highlighting the comparative analysis between random forest and HARDenseRNN across different pre-processing configurations, using inertial data acquired from the most affected upper limb via a GeneActiv smartwatch.  
Overall, HARDenseRNN with a segment size of 250 and S1 labeling emerges as the best-performing model and pre-processing configuration, achieving the highest recall of 91.54%. Although random forest is more stable across different configurations, HARDenseRNN's higher recall makes it the more effective method for movement disorder classification in this context. Detailed results of the 12 models evaluated for movement disorder classification can be seen in Figure 10. For random forest movement disorder classification, this study used a power set approach to perform multi-label classification. On the other hand, HARDenseRNN is able to perform multi-label classification, without the need of the power set approach, by using three output nodes, one for each anomaly. The confusion matrix for HARDenseRNN (across each node) using a window size of 250 and S1 labeling is shown in Figure 7.