## 1 Introduction  
[PERSON]'s disease, a neurological disorder stemming from nerve cell degeneration in the brain's substantia nigra, currently lacks a cure [1]. Diagnosis of [PERSON]'s relies not on X-rays or blood tests but on observable symptoms affecting daily activities [2], such as freeze of gait (freezing while walking or performing tasks), tremors, dyskinesia (uncontrolled movements), and bradykinesia (slowed movements). These symptoms pose significant risks, such as bradykinesia while crossing streets or dyskinesia while handling sharp objects like knives, potentially leading to harm [3]. Timely detection of these symptoms can greatly mitigate such risks, allowing for prompt alerts and the implementation of strategies to reduce their impact [4].  
Devices such as smartwatches, smart fitness bands, and smartphones are equipped with built-in inertial sensors, including accelerometers and gyroscopes. These sensors can be non-invasively attached to the human body, enabling real-time data analytics and allowing the devices to provide immediate alerts and cues. These devices utilize inertial data, making them versatile in various applications, including terrain classification [5], fall detection [6], and human activity recognition [7]. Previous studies and the widespread availability of inertial measurement units (IMUs) show promise in leveraging these data for Parkinson's disease (PD) diagnosis and monitoring [8; 9; 10; 11; 12]. IMUs typically include accelerometers, gyroscopes, and sometimes magnetometers, all commonly found in most of the modern digital gadgets.  
Driven by the high performance achieved through the utilization of inertial data in various applications, this study demonstrates the critical role of wearable technology such as smartwatches and fitness bands in anomalous event detection and classification for the PD patients. These devices enable continuous monitoring and analysis of movement patterns by capturing real time inertial data, thus facilitating timely alerts and medical interventions. Our paper introduces several novel contributions to wearable technology for PD management:  
* An end-to-end pipeline for real-time movement disorder detection, achieving an inference time of 80 ms, and multi-label classification (inference time of 165 ms) of tremors, dyskinesia, and bradykinesia.
* The proposed model is very lightweight (around 9 MBs of size), which is optimal for deployment on edge devices such as smartwatches and fitness bands.
* This study proposes two auto-labeling techniques (early and late labeling) to identify optimal pre-processing configurations and evaluates the proposed model using three different window sizes (50, 150, 250) for optimal signal segmentation.
* Evaluation of two modeling techniques--manual feature crafting for a machine learning pipeline and processing raw inertial data for a deep learning pipeline--to find the optimal approach. A detailed comparison between the two modeling techniques can be found in the Results Section. For movement disorder detection, the deep learning approach achieves a recall of 93%. Similarly, for movement disorder classification, the deep learning approach achieves a recall score of 91.54%.