Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.

Purpose: The purpose of this study was to introduce an orthogonal experimental design (OED) to improve the efficiency of building and optimizing models for freezing of gait (FOG) prediction.Methods: A random forest (RF) model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG (preFOG). An OED was introduced to optimize the feature extraction parameters.Results: The main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction time (MPT) were 27%, 68%, and 2.99 s, respectively.Conclusion: The OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model.

The definition of Parkinson’s disease (PD) is undergoing a profound transformation from a “clinical syndrome” to a “biological entity”, with development of two objective biological classification systems, the NSD-ISS (Neuronal Alpha-Synuclein Disease Integrated Staging System) and the SynNeurGe framework. Multimodal diagnostic tools further improve the detection of PD. This review synthesizes advances in three key domains of PD detection. First, fluid and tissue biomarkers, particularly using α-synuclein (αSyn) seed amplification assays, allow detection of synucleinopathy in cerebrospinal fluid, blood, saliva, and skin. This supports pathological diagnosis and differential classification. Extracellular vesicles provide cell-type-specific cargo profiles, while neurofilament light chain indicates neuroaxonal injury. Second, neuroimaging captures in vivo pathology. MRI identifies nigral degeneration (nigrosome-1 loss, iron accumulation, neuromelanin depletion), MRS reveals metabolic and neurotransmitter imbalances, and αSyn positron emission tomography tracers enable direct visualization of aggregation. Third, digital biomarkers derived from wearable devices, videos, and audios quantify real-world motor and non-motor symptoms, enabling continuous, ecological monitoring. Integration of these complementary biomarker streams is essential for biological definition and stratification of PD patients, and development of targeted therapies. Standardization of assays, multicenter validations, and clear guidance on who should be tested, when testing is appropriate, and how results should inform diagnosis, stratification, or monitoring are required for the translation of these methods into clinical practice in PD.

Objective: Gait and posture symptoms—such as gait impairments, postural instability, and posture deformations—are common in Parkinson’s disease (PD) and closely linked to falls. Traditional assessments using clinical scales are time-consuming and prone to subjective bias. This study aims to predict the severity of gait and posture symptoms using data collected from wearable sensors during a single laboratory-based walking assessment, providing an objective, efficient, and automated evaluation approach.Methods: Sensor-based gait parameters were collected from 225 PD participants (mean age 63.15 ± 10.46 years) through a standardized walking assessment. The dataset was randomly split into a training set (80%) and an independent test set (20%) with balanced age, sex, and PD duration. Two machine learning models—extreme gradient boosting (XGBoost) and support vector machine (SVM)—were trained to predict scores for five gait and posture items (#3.9–3.13) from the MDS-UPDRS III.Results: XGBoost was chosen as the final model due to its better performance than SVM. Across all five gait and posture items, the models achieved over 80% acceptable accuracy. For items #3.9–#3.11, absolute accuracy surpassed 70%, and macro-F1 scores were above 0.60 in leave-one-out cross-validation (LOOCV). The model’s performance on the independent test set matched LOOCV results, confirming robustness. A total of 35, 35, 30, 30, and 40 gait features were selected for the predictive models of items #3.9–#3.13, respectively. Among these, key features with significant clinical relevance were identified. For example, Effective Trial Duration (R = 0.522, p < 0.001) had a positive correlation, while Shank—Swing RoM—mean (max) (R = −0.629, p < 0.001) had a negative correlation with scores on item #3.10. In addition, 180° Turn—Steps – mean (R = 0.482, p < 0.001) had a positive correlation with scores on item #3.11. These findings align with known clinical manifestations, reinforcing the clinical relevance of the identified gait features.Conclusion: This study demonstrates the feasibility of using wearable sensor data to objectively assess gait and posture symptoms in PD. Though conducted in a clinical setting, the approach may support clinicians through consistent assessments and more frequent monitoring, with potential for future home-based use to enable longitudinal symptom tracking.

Background and purpose: The aim was to demonstrate the feasibility, reliability and validity of an in‐home remote levodopa challenge test (LCT), as delivered through an online platform, for patients with Parkinson's disease (PwPD).Methods: Patients with Parkinson's disease eligible for deep brain stimulation surgery screening were enrolled. Participants sequentially received an in‐home remote LCT and an in‐hospital standard LCT (separated by 2.71 weeks). A modified Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III omitting rigidity and postural stability items was used in the remote LCT. The reliability of the remote LCT was evaluated using the intraclass correlation coefficient and the concurrent validity was evaluated using the Pearson's correlation coefficient r between the levodopa responsiveness of the remote and standard LCT.Results: Out of 106 PwPD screened, 80 (75.5%) completed both the remote and standard LCT. There was a good reliability (intraclass correlation coefficient 0.81, 95% confidence interval 0.69–0.88) and a strong correlation (r = 0.84, 95% confidence interval 0.77–0.90) between the levodopa responsiveness of the remote and standard LCT. The mean cost for PwPD was estimated to be reduced by 91% by using the remote LCT.Conclusion: The remote LCT is feasible, reliable and valid and may reduce healthcare‐related costs for PwPD and their caregivers.