## 2 Literature Review  
Many of the recent studies employ inertial data for detecting and characterizing PD and its symptoms. For instance, one study focused on freeze of gait (FOG), a common PD symptom affecting gait and increasing fall risk, achieving a 91.5% F1-score using time-frequency features and convolutional neural networks [13]. [PERSON] et al. [14] targeted bradykinesia by analyzing upper limb inertial signals with a multi-layer perceptron and achieved an accuracy of 85%. Similarly, FOG is addressed in another study, which utilized inertial sensors and a novel evaluation metric called GaitScore [15]. They achieved 97% sensitivity and 87% specificity. Wearable inertial sensors offer promise for monitoring axial impairments in PD. Integration with machine learning techniques yielded low root mean square error (RMSE) and posture instability/gait difficulty scores (PIGD scores) [16], and validation studies confirmed their reliability in capturing gait parameters [17]. Investigations into hand tremors and levodopa effects on gait parameters achieved high accuracy using inertial sensors and advanced machine learning methods [18; 19]. Studies on stride segmentation using hidden Markov models achieved a 92.1% F1-score on real-time gait data [19], confirming the reliability of wearable sensors for gait analysis under supervised and unsupervised settings [20]. [PERSON] et al. [21] employed neural networks on inertial data in early PD detection,demonstrating the effectiveness of inertial data and machine learning in PD diagnosis and management. [PERSON] et al. [22] presented an interpretable CNN-based architecture and used spatio-temporal features for [PERSON]'s disease detection, achieving an accuracy of 98%. [PERSON] et al. [23] utilized a deep learning model on a time-series gait dataset acquired from an IMU mounted on a subject's leg to differentiate between healthy persons and persons with mild Parkinson's disease. [PERSON] et al. [24] proposed two deep learning architectures (LN-inception and InSEption) to predict FOG episodes, achieving an F1-score of 97%. Inertial data require pre-processing before being used for machine learning or fed directly into deep learning models, as shown in many studies. Pre-processing steps involve noise removal using techniques like simple moving average [25; 26]. This cleaned signal is then decomposed into smaller segments using techniques like peak/valley detection [25], or by dividing signal into windows of fixed length and stride [27]. Labeling segmented data follows various approaches based on the use case; straightforward cases apply the same label to all segments, e.g., gender identification [25], while cases where the label for the whole signal is not same, e.g., occurrence of tremor episodes within the gait of a patient suffering from PD, require more sophisticated segment-labeling methods. With non-homogeneous segments, methods using the mode as the overall segment label are commonly used, as in [26], or treating the segment as an anomalous one even if a smaller subset of it is anomalous [27]. Features from time, frequency, or wavelet domains are computed for each segment for machine learning models, improving model accuracy but increasing the processing time. Deep learning models offer improved computation time and accuracy by directly using segmented and labeled raw data for training and inference [28]. Apart from using inertial data, diverse data sources have been used to detect and quantify PD, such as electroencephalography (EEG), electromyography (EMG), speech, and vision. Commonly employed deep learning models include CNNs and RNNs, achieving high accuracies in various tasks. For instance, a combination of CNN+RNN classifies PD patients based on EEG with 99.2% accuracy [29], while EMG readings detect motor fluctuations with 99% accuracy [30]. Speech- and voice-based models also show promising results; however, real-world noise affects reliability [31; 32; 33; 34; 35].