Search Results (118)

Parkinson’s disease (PD) is a neurodegenerative disorder affecting millions worldwide, characterized by motor symptoms such as tremor, rigidity, and bradykinesia that significantly impair daily life. The current diagnosis and monitoring rely primarily on clinical observations and rating scales (e.g., the MDS-UPDRS), which are subjective and limited in detecting subtle motor alterations, leading to inter- and intra-rater variability. In recent years, wearable sensors such as surface electromyography (sEMG) and inertial measurement units (IMUs) have emerged as non-invasive tools for quantifying neuromuscular activity and motor performance in PD. When combined with machine learning (ML) and deep learning (DL) techniques, these signals enable the development of models for disease detection, patient classification, and symptom severity assessment. This review provides a structured overview of recent ML and DL approaches applied to surface electromyography for PD monitoring, addressing a gap in the current literature. It analyzes data acquisition strategies, preprocessing techniques, feature extraction methods, model architectures, and evaluation protocols across tasks such as diagnosis, tremor analysis, freezing of gait detection, and gait assessment. Despite promising results, key challenges remain, including limited dataset size, lack of standardization, and poor generalization. Finally, this work highlights emerging trends and identifies a representative processing pipeline to support real-world clinical translation. Full article

Background/Objectives: Freezing of gait (FOG) and sleep disturbances are common in people with Parkinson’s disease (PwPD). A bidirectional association between them has been suggested, but quantitative evaluations are scarce. This study aimed to compare sleep disturbances in mild-to-moderate PwPD with (PD+FOG) and without FOG (PD−FOG), and to assess the association between FOG severity and sleep parameters. Methods: Data from 54 PwPD with disease stage <4 and no severe cognitive decline were included (27 PD+FOG and 27 propensity score-matched for age, sex, and disease duration PD−FOG). Demographics and clinical variables were collected. Clinical assessment included the new freezing of gait questionnaire (NFOG-Q), Parkinson’s Disease Sleep Scale (PDSS-2), Epworth Sleepiness Scale (ESS) and Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Mann–Whitney U, Fisher’s exact and Spearman’s tests were used for group comparisons and correlations, respectively. Results: Significant differences were observed between PD+FOG and PD−FOG groups in MDS-UPDRS part II (p = 0.011) and part IV (p = 0.011), with higher scores in PD+FOG participants. No significant differences were found in PDSS-2 or ESS between the two groups. A significant moderate positive correlation was found between NFOG-Q score and PDSS-2 (ρ = 0.416; p = 0.044) in PD+FOG participants. Conclusions: FOG severity was positively associated with sleep disturbances within the PD+FOG group. However, no significant difference in sleep quality or excessive daytime sleepiness was found between PD+FOG and PD−FOG after propensity score matching. PD+FOG participants experienced more severe motor complications and greater impairment in daily activities compared to PD−FOG. Full article

Cross-topic stance detection degrades when encoders entangle stance signals with topic-specific vocabulary, causing representations that fail to transfer to unseen targets. Existing methods commit to either topic-invariant or topic-aware representations and apply the same strategy uniformly to every input, sacrificing complementary information. We propose ReDyGait, a three-stage framework that disentangles these two types of signals through dedicated contrastive pre-training and recombines them adaptively at inference time. Stage 1 trains a topic-invariant encoder with supervised contrastive loss over cross-topic positives. Stage 2 trains a topic-contextual encoder with bidirectional pair contrastive loss over within-topic positives; both stages employ topic-aware hard negative mining to prevent shortcut learning. Stage 3 freezes the two contrastive encoders and learns a gating network that produces per-instance weights over invariant, contextual, and base-encoder pathways. On VAST, ReDyGait achieves a macro-averaged F1 of 0.782 in the zero-shot setting and 0.752 in the few-shot setting, improving over the strongest baseline by 1.1 points in both; on SEM16t6 in a leave-one-target-out setup, ReDyGait reaches an average F1 of 0.612. Analysis of the learned gate weights shows that the model shifts toward the invariant pathway for unfamiliar topics and toward the contextual pathway when topic-specific patterns are available, confirming that the disentanglement operates as intended. Full article

Background/Objectives: Freezing of gait (FoG) is a disabling motor manifestation of Parkinson’s disease (PD) associated with impaired neural control of locomotion and increased gait variability. Quantitative characterization of gait kinematics may provide biomechanical insight into FoG-related instability, particularly under different dopaminergic states. Methods: This pilot study evaluated sagittal-plane knee kinematics in healthy individuals (n = 27) and patients with PD. (n = 8) under OFF and ON dopaminergic medication conditions using two-dimensional videogrammetry (Kinovea^®). Knee flexion–extension trajectories were time-normalized to 0–100% of the gait cycle, and group ensemble profiles (mean ± SD) were computed. Results: Phase-specific range of motion (ROM), within-subject variability, and interlimb coordination were quantified. Interlimb coordination was assessed using Pearson’s correlation coefficients (r) and cross-correlation lag analysis computed per subject and summarized statistically across groups. Compared with healthy participants, PD patients in the OFF state exhibited significantly reduced knee ROM during stance and swing (p < 0.05), accompanied by increased kinematic variability and disrupted temporal coordination. Interlimb correlation was significantly lower in PD OFF compared to healthy gait groups (p = 0.010), with larger temporal lags, indicating impaired bilateral synchronization. Following medication intake (ON state), knee excursion increased and interlimb coordination partially improved; however, correlation values and timing symmetry did not fully normalize to healthy levels. Conclusions: These findings demonstrate that sagittal-plane knee kinematics and interlimb coordination metrics derived from low-cost 2D videogrammetry are sensitive to the dopaminergic state and reveal persistent neuromotor deficits in PD. The proposed framework provides an interpretable and accessible approach for characterizing gait organization in Parkinson’s disease and supports future integration with clinical assessment and longitudinal monitoring. Full article

Effective Parkinson’s disease (PD) management is hindered by the intermittent nature of clinical snapshots and the discomfort of rigid monitoring hardware. This review critically evaluates the synergy between flexible bioelectronics and artificial intelligence (AI) for continuous remote monitoring. Our analysis reveals that while material innovations have achieved milligram-level sensitivity, a significant ‘translational gap’ persists due to limited validation in real-world environments and small cohort sizes. We conclude that multimodal fusion architectures are essential for accurately mapping digital biomarkers to clinical gold standards such as MDS-UPDRS. By leveraging edge AI for privacy and closed-loop feedback for intervention, this integration facilitates the transition from reactive clinical visits to proactive, personalized digital home-care ecosystems. Full article

Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of FoG, current treatments have proven ineffective in managing episodes. In recent years, machine learning algorithms have been leveraged to derive actionable clinical insights from biomedical datasets. As a manifestation of neuromechanical dysfunction, impending FoG episodes may be characterized through data collected by wearable devices and sensors. Objective: This scoping review evaluates the current landscape of machine and deep learning-derived biomarkers to enhance the personalized management of FoG. Methods: This scoping review was conducted using established methodological frameworks for scoping reviews and is reported in accordance using the PRISMA-ScR checklist. Three databases were queried, with screening yielding 60 studies. Results: Thirty-nine papers reported on deep learning techniques, with the most common architectures being convolutional neural networks and long short-term memory models. Conclusions: Inertial measurement units, which can be worn on various locations, may be a promising modality for practical implementation. To generate closed-loop FoG therapies, algorithms can be integrated into real-time systems like robotic exoskeletons or adaptive deep brain stimulation. Future work in generating datasets from ambulatory devices, as well as distributed computing strategies, may lead to real-time FoG management. Full article

Gait is a sensitive marker of mobility decline and fall risk, motivating unobtrusive sensing methods that can extract spatiotemporal parameters outside specialized gait laboratories. This paper presents a physics-based comparison of two millimeter-wave frequency-modulated continuous-wave (FMCW) radar deployment paradigms using a low-cost, system-on-chip (SoC) 60 GHz Infineon BGT60TR13C radar sensor: (i) a fixed (tripod-mounted) corridor observer and (ii) a shoe-mounted body-centric configuration attached to the medial side of the left shoe. Four healthy adult author-participants performed repeated 30 s corridor trials under five gait styles (regular, slow, fast, simulated festination, and simulated freezing-of-gait), including brief pauses during turns; an empty-corridor recording was acquired to characterize static clutter. Step events were detected using peak-picking on foot-related velocity envelopes with adaptive thresholds, and step count, cadence, step time, and step-time variability were derived. Performance of the fixed and shoe-mounted configurations was quantitatively compared to video ground truth using mean absolute percentage error (MAPE) for step count estimation. Across all gait styles, the shoe-mounted FMCW radar consistently reduced step-count error relative to the fixed corridor-mounted configuration, with the largest gains under irregular patterns (e.g., festination: 37.1% fixed vs. 9.6% shoe-mounted). These findings highlight the advantages of body-centric millimeter-wave radar sensing and support low-cost SoC radar as a pathway toward wearable, privacy-preserving gait monitoring in real-world environments. Full article

Parkinson’s disease (PD) is the fastest-growing neurodegenerative disease affecting 90 thousand new Americans each year. PD includes motor and non-motor symptoms, resulting in progressive disability and difficulty in completing activities of daily living. Freezing of Gait (FoG) is one of the common disabling symptoms of PD, characterized by difficulties in initiating walking, resulting in gait abnormalities and increased risk of falling (RoF) and fear of falling (FoF). Clinical management of FoG is difficult as it is minimally responsive to both pharmacological and surgical interventions. In fact, these interventions can paradoxically worsen of FoG. Additionally, PD patients with FoG have reported worse health-related quality of life (HR-QoL) due to limitations in mobility, activities of daily living (ADL), bodily discomfort, stigma, and social isolation. Despite its increasing treatment and management of FoG is difficult due to its paroxysmal and heterogeneous nature. Therefore, there is a growing need for effective, evidence-based management and intervention approaches for FoG. Some current techniques used to manage FoG are physical therapy, exercise, gait training, and balance training; however, due to a lack of patient adherence, accessibility concerns, and the need for continuous supervision and individualized feedback, the long-term effectiveness of these interventions remains limited and challenging to achieve in real-world settings. A new promising avenue for managing PD is the use of wearable technology, which can provide audiovisual, via augmented and virtual reality (AR/VR), and tactical cueing to offset FoG, thereby enhancing independence in PD patients. In this comprehensive review, we will provide an overview of the symptoms, monitoring, and treatment of PD, with a focus on the neuroanatomy and treatment of FoG. We will review and critique the extant literature on the use of AR/VR technology in the management of FoG. Finally, the challenges and risks associated with wearable technology in FoG management will also be identified. Full article

Freezing of gait (FoG) affects 38–65% of advanced Parkinson’s disease patients, yet automated detection algorithms are often validated solely on laboratory datasets. This study quantifies the critical performance gap between laboratory and real-world performance—a prerequisite for clinical deployment. Using temporal convolutional networks (TCNs), we trained models on two public datasets representing ecological extremes: a daily living dataset (Figshare; n = 35, single-sensor) and a laboratory dataset (DAPHNET; n = 10, multi-sensor). We compared five training configurations to address class imbalance. Results showed that F1-based early stopping outperformed Area Under the Curve (AUC)-based stopping by 47% (F1: 0.55 vs. 0.37, p = 0.0008). Combining multiple imbalance corrections (focal loss, weighting, sampling) paradoxically degraded precision to 33% due to a ~60-fold over-weighting of the minority class. Most importantly, cross-dataset validation revealed an 83% performance gap: laboratory F1 reached 0.9999 ± 0.0002, whereas daily living F1 dropped to 0.55 ± 0.26 (p < 0.0001), with a 1299-fold increase in variance. These findings demonstrate that laboratory success does not guarantee real-world utility. We propose that the observed gap represents a “deployment gap” reflecting the combined influence of environmental complexity, sensor constraints, and physiological variability. These results provide an empirical framework for evaluating deployment readiness of wearable FoG detection systems and offer concrete training strategy recommendations for clinical translation. Full article

Axial postural abnormalities in Parkinson’s Disease (PD) are traditionally assessed using clinical rating scales, although picture-based assessment is considered the gold standard. This study evaluates the reliability and clinical relevance of two markerless body-tracking frameworks, the RGB-D-based Microsoft Azure Kinect (providing the reference KIN_3D model) and the RGB-only Google MediaPipe Pose (MP), using a synchronous dual-camera setup. Forty PD patients performed a 60 s static standing task. We compared KIN_3D with three MP models (at different complexity levels) across horizontal, vertical, sagittal, and 3D joint angles. Results show that lower-complexity MP models achieved high congruence with KIN_3D for trunk and shoulder alignment (ρ > 0.75), while the lateral view significantly improved tracking of sagittal angles (ρ ≥ 0.72). Conversely, the high-complexity model introduced significant skeletal distortions. Clinically, several angular parameters emerged as robust metrics for postural assessment and global motor impairments, while sagittal angles correlated with motor complications. Unexpectedly, a more upright frontal alignment was associated with greater freezing of gait severity, suggesting that static postural metrics may serve as proxies for dynamic gait performance. In addition, both RGB-only and RGB-D frameworks effectively discriminated between postural severity clusters. While the higher-complexity MP model should be avoided due to inaccurate 3D reconstructions, our findings demonstrate that low- and medium-complexity MP models represent a reliable alternative to RGB-D sensors for objective postural assessment in PD, facilitating the widespread application of objective posture measurements in clinical contexts. Full article

Background: Parkinson’s disease (PD) is characterized by motor disturbances that significantly impact balance, gait, and quality of life. Personalized Rhythmic Auditory Stimulation (pRAS) is an emerging rehabilitative approach that utilizes auditory entrainment to improve step and gait control. The aim of this systematic review is to critically summarize the data from the most recent evidence concerning the use of pRAS in gait rehabilitation for patients with Parkinson’s disease. Methods: A systematic review was conducted following PRISMA guidelines, including records that evaluated music-based or technological interventions based on personalized RAS. Primary outcomes included spatiotemporal gait parameters and distance covered. Results: Ten studies were included in the analysis. All the studies reported clinically relevant improvements: increases in gait speed, step length, and amplitude. Moreover, a reduction in freezing of gait episodes (up to 36%), greater walking distance, and good adherence were reported. Conclusions: Personalized, adaptive, or on-demand solutions proved more effective than traditional forms of cueing. Moreover, the available evidence suggests that pRAS constitutes an effective and safe rehabilitative option for gait disturbances in PD. However, further studies with larger sample sizes and prolonged follow-up periods are necessary to evaluate its long-term impact and transferability into clinical practice. Full article

Objective: This study aimed to systematically evaluate the therapeutic effects of repetitive transcranial magnetic stimulation (rTMS) on gait, motor function, and balance in patients with Parkinson’s disease (PD) and identify optimal stimulation parameters for clinical application. Methods: This systematic review and meta-analysis of randomized controlled trials (CTs) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, EMBASE, Cochrane Central, Scopus, and Ovid-LWW were searched until December 2024 for RCTs evaluating the effects of rTMS on PD-related gait, balance, or motor outcomes. Nineteen studies (n = 547) met the inclusion criteria. Data on study characteristics, rTMS protocols (frequency, target area, pulses, session duration, number of sessions, and treatment duration), and outcome measures (freezing of gait questionnaire [FOG-Q], gait speed, Unified Parkinson’s Disease Rating Scale Part III [UPDRS-III], UPDRS total, and timed up and go [TUG] test) were extracted. Effect sizes (Hedges’ g) were pooled using inverse variance meta-analysis, heterogeneity was assessed using I², and publication bias was assessed using funnel plots and Egger’s regression. Results: rTMS produced significant improvements in gait freezing (FOG-Q: g = −0.74; 95% confidence interval [CI] [−1.05, −0.43]; p < 0.001), gait speed (g = 0.62; 95% CI [0.29, 0.95]; p < 0.001), and motor symptoms (UPDRS-III: g = −0.42; 95% CI [−0.70, −0.15]; p = 0.003). No significant effects were observed for UPDRS total (g = 0.18; p = 0.58) or balance (TUG, g = −0.29; p = 0.06). Egger’s test indicated publication bias for gait speed (p = 0.016); however, trim-and-fill imputed zero studies. Subgroup analyses indicated that high-frequency stimulation of the supplementary motor area (SMA) for ≥20 min over 10 sessions (total duration <2 weeks or ≥2 weeks) optimally improved gait speed, whereas low-frequency stimulation targeting M1 and SMA with >1000 pulses per session for 20 min over 10 sessions within <2 weeks most effectively improved the UPDRS-III scores. Conclusions: rTMS exerts moderate and significant benefits on gait and motor performance in PD, particularly when tailored protocols involving SMA or M1 stimulation are employed. High-frequency SMA protocols improve gait speed, whereas low-frequency M1/SMA protocols optimize motor symptom relief. These findings provide evidence-based guidance for rTMS implementation in PD rehabilitation. Full article

Objectives: With the development of advanced neuroimaging techniques, including resting-state functional magnetic resonance imaging and diffusion tensor imaging, Parkinson’s disease (PD) has increasingly been recognized as a complex brain network disorder. In this review, we summarized research on brain networks in PD to elucidate the network abnormalities underlying its four major motor symptoms and to identify the networks modulated by deep brain stimulation (DBS). Materials and Methods: We searched PubMed and Web of Science for the most recent literature on brain network alterations in PD. Eligible studies included those investigating the general PD network (n = 10), symptom-specific networks—tremor-dominant (n = 13), postural instability and gait disorder (n = 9), freezing of gait (n = 9), akinetic-rigidity (n = 3)—as well as DBS-modulated networks (n = 14). Based on these studies, we integrated the findings and used BrainNet Viewer to generate schematic network visualizations. Results: The symptom-specific networks exhibited common abnormalities within the sensorimotor network. Evidence from DBS studies suggested that therapeutic effects were associated with modulation of the motor cortex through both functional and structural connectivity. Moreover, the four motor symptoms each demonstrated distinct network features. Specifically, the tremor network was characterized by widespread alterations in the cortico-thalamic-cerebellar circuitry; the postural instability and gait disorder network showed more severe disruptions within the striatum and visual cortex; the freezing of gait network exhibited disruptions in midbrain regions, notably the pedunculopontine nucleus; and the akinetic-rigidity network involved changes in cognition-related networks, particularly the default mode network. Conclusions: PD motor symptoms exhibit both distinct network features and shared alterations within the sensorimotor network. DBS modulates large-scale brain networks, especially motor-related networks, contributing to the alleviation of motor symptoms. Characterizing symptom-specific networks may support precision DBS target selection and parameter optimization. Full article

Freezing of gait (FoG) is a common motor symptom in advanced Parkinson’s disease, leading to falls, disability, and reduced quality of life. Although cueing systems using visual or auditory stimuli can help patients resume walking, existing solutions are often expensive, uncomfortable, and conspicuous. ParCuR (Parkinson Cueing and Rehabilitation) is a compact, ankle-worn wearable integrating an inertial sensor, haptic stimulator, and AI-based software. It was developed to detect FoG episodes in real time and provides automatic sensory cues to assist patients with Parkinson’s Disease (PwP). A classifier was trained for FoG detection using the DAPHNet dataset, comparing patient-specific and patient-independent models. While a small-scale trial with PwP assessed usability and reliability. ParCuR is watch-sized (35 × 41 mm), discreet, and comfortable for daily use. The online detection algorithm triggers stimulation within 0.7 s of episode onset and achieves 94.9% sensitivity and 91.3% specificity using only 14 frequency-based features. Preliminary trials confirmed device feasibility and guided design refinements. This low-cost, wearable solution supports personalized, real-time FoG detection and responsive cueing, improving patient mobility while minimizing discomfort and continuous stimulation habituation. Full article

The LRRK2+ SAA− cohort of Parkinson’s disease (PD), characterized by the absence of hallmark α-synuclein pathology, remains under-explored. This limits opportunities for early detection and targeted intervention. This study analyzes data from this under-characterized subgroup and compares it with the LRRK2+ SAA+ cohort using longitudinal data from the Parkinson’s Progression Markers Initiative (PPMI). The PPMI dataset includes 115 LRRK2+ patients (70 SAA+, 45 SAA−) across 52 features encompassing clinical assessments, cognitive scores, DaTScan SPECT imaging, and motor severity. DaTScan binding ratios were selected as imaging-based indicators of early dopaminergic loss, while NP3TOT (MDS-UPDRS Part III total score) was used as a gold-standard clinical measure of motor symptom severity. Linear mixed-effects models were then applied to evaluate longitudinal predictors of DaTScan decline and NP3TOT progression, and statistical analyses of group comparisons revealed distinct drivers of symptoms differentiating SAA− from SAA+ patients. In SAA− patients, a decline in DaTScan was significantly associated with thermoregulatory impairment (p-value = 0.019), while NP3TOT progression was predicted by constipation (p-value = 0.030), sleep disturbances (p-value = 0.046), and longitudinal time effects (p-value = 0.043). In contrast, SAA+ patients showed significantly lower DaTScan values compared to SAA− (p-value = 0.0004) and stronger coupling with classical motor impairments, including freezing of gait (p-value = 0.016), rising from a chair (p-value = 0.007), and turning in bed (p-value = 0.016), along with cognitive decline (MoCA clock-hands test, p-value = 0.037). These findings support the hypothesis that LRRK2+ SAA− patients follow a distinct pathophysiological course, where progression is influenced more by autonomic and non-motor symptoms than by typical motor dysfunction. This study establishes a robust, multimodal modeling framework for examining heterogeneity in genetic PD and highlights the utility of combining DaTScan, NP3TOT, and symptom-specific features for early subtype differentiation. These findings have direct clinical implications, as stratifying LRRK2 carriers by SAA status may enhance patient monitoring, improve prognostic accuracy, and guide the design of targeted clinical trials for disease-modifying therapies. Full article