## 1 Introduction  
Parkinson's Disease (PD) is one of the most prevalent neurological diseases. The global prevalence of PD patients is expected to reach 12 million cases by 2040 [1]. The pathophysiology of PD is extensively researched and has been linked with genetic and environmental risk factors [2]. The main neuropathological hallmarks of PD include neuroinflammation, degeneration of dopaminergic neurons in the substantia nigra pars compacta, and accumulation of misfolded \(\alpha\)-synuclein proteins as intra-cytoplasmic Lewy bodies and neurites [3, 4]. The manifestations of PD include both motor and non-motor symptoms. The cardinal motor symptoms of PD are bradykinesia, rigidity, tremor, and postural instability [2; 5], whilst the non-motor symptoms include depression, cognitive decline and dementia, sleep disorders, dysphagia, constipation, urinary dysfunction, and orthostatic hypotension, among others [5; 6; 7; 8]. At the moment, there is no cure for PD, however, the therapeutic process includes among others the admission of drugs for symptomatic relief, mainly focusing on dopamine replacement strategies [9]. Based on patient's clinical characteristics, treatment may include surgical approaches, such as deep brain stimulation (DBS) [10]. The diagnosis of [PERSON]'s disease is, until today, mainly based on the clinical assessment of the patient [11]. The Movement Disorder Society--Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is considered the gold standard for the assessment and monitoring of [PERSON]'s disease [12], followed by the well-established Hoehn and Yahr (HY) scale [13]. Gait analysis is critical for managing and monitoring the progression of PD symptoms. Wearable sensor-enabled systems have been developed to extract numerous aspects of PD motor symptoms, such as gait abnormalities, imbalance problems and tremor, which cannot be derived merely from the clinical picture of the patient [14]. There is evidence that gait patterns differentiate between PD patients and healthy controls [15; 16]. There has been a surge of interest in using machine learning and deep learning approaches that utilize sensor signals (i.e., accelerometers, gyroscopes, pressure sensors) and/or videos to monitor and forecast PD progression [14; 17; 18; 19; 20; 21; 22; 23; 24]. However, video-based motion analysis systems require expensive equipment and are limited in equipped-indoor environments, while wearable sensors are a more affordable solution, providing a method for long-term monitoring under real-life conditions [25; 26; 27; 28]. These research results demonstrate promising potential for the deployment of systems for the monitoring and management of PD, based on sensor signals and machine-learning methods. Although a lot of studies focus on motor symptoms, and specifically, gait analysis, and there are standardized assessment tests, we observe that a comprehensive protocol is lacking, i.e., the studies are exploring only one type of test, they include walking at normal pace only, they often use unbalanced datasets, and in several cases a control group is lacking. Another important aspect that needs to be considered is the need for the parallel assessment of PD participants by specialized clinicians using state-of-the-art quantification metrics, such as the MDS-UPDRS [12]. These issues are also observed, in full or in part, in the limited number of publicly available datasets that include sensor-based gait data and focus on PD patients, as shown in Table 1. Specifically, the Gait in Parkinson's Disease dataset [29], includes the vertical ground reaction force records of a large number of participants (93 PD patients and 73 healthy controls) along with demographic information and scores of PD scales (the H and Y staging and/or the UPDRS), however only self-selected pace of walking for 2 min was recorded, with a subset of participants to also perform a dual tasking test, while ON/OFF medications states were not addressed.