Intra and inter-rater remote assessment of bradykinesia in Parkinson's disease.

INTRODUCTION: Reliable assessment of individuals with Parkinson's disease (PD) is essential for providing adequate treatment. Clinical assessment is a complex and time-consuming task, especially for bradykinesia, since its evaluation can be influenced by the degree of experience of the examiner, patient collaboration and individual bias. Improvement of the clinical evaluation can be obtained by considering assessments from several professionals. However, this is only true when inter and intra-rater agreement are high. Recently, the Movement Disorder Society highlighted, during the COVID-19 pandemic, the need to develop and validate technologies for remote assessment of the motor status of people with PD. Thus, this study introduces an objective strategy for the remote evaluation of bradykinesia using multi-specialist analysis. METHODS: Twelve volunteers with PD participated and these were asked to execute finger tapping, hand opening/closing and pronation/supination movements. Each task was recorded and rated by fourteen PD health experts for each patient. The scores were assessed on an individual basis. Intra and inter-rater agreement and correlation were estimated. RESULTS: The results showed that agreements and correlations between experienced examiners were high with low variability. In addition, group analysis was noted as possessing the potential to solve individual inconsistency bias. CONCLUSION: Furthermore, this study demonstrated the need for a group with prior training and experience, along with indicating the importance for the development of a clinical protocol that can use telemedicine for the evaluation of individuals with PD, as well as the inclusion of a specialized mediating group. In Addition, this research helps to the development of a valid remote assessment of bradykinesia.

Slowing of Movements in Healthy Aging as a Rational Economic Response to an Elevated Effort Landscape.

Why do we move slower as we grow older? The reward circuits of the brain, which tend to invigorate movements, decline with aging, raising the possibility that reduced vigor is due to the diminishing value that our brain assigns to movements. However, as we grow older, it also becomes more effortful to make movements. Is age-related slowing principally a consequence of increased effort costs from the muscles, or reduced valuation of reward by the brain? Here, we first quantified the cost of reaching via metabolic energy expenditure in human participants (male and female), and found that older adults consumed more energy than the young at a given speed. Thus, movements are objectively more costly for older adults. Next, we observed that when reward increased, older adults, like the young, responded by initiating their movements earlier. Yet, unlike the young, they were unwilling to increase their movement speed. Was their reluctance to reach quicker for rewards due to the increased effort costs, or because they ascribed less value to the movement? Motivated by a mathematical model, we next made the young experience a component of aging by making their movements more effortful. Now the young responded to reward by reacting faster but chose not to increase their movement speed. This suggests that slower movements in older adults are partly driven by an adaptive response to an elevated effort landscape. Moving slower may be a rational economic response the brain is making to mitigate the elevated effort costs that accompany aging.

Objective assessment of the effects of opicapone in Parkinson's disease through kinematic analysis.

BACKGROUND: Opicapone (OPC) is a third-generation, selective peripheral COMT inhibitor that improves peripheral L-DOPA bioavailability and reduces OFF time and end-of-dose motor fluctuations in Parkinson's disease (PD) patients. OBJECTIVES: In this study, we objectively assessed the effects of adding OPC to L-DOPA on bradykinesia in PD through kinematic analysis of finger movements. METHODS: We enrolled 20 treated patients with PD and motor fluctuations. Patients underwent two experimental sessions (L-DOPA, L-DOPA + OPC), separated by at least 1 week. In each session, patients were clinically evaluated and underwent kinematic movement analysis of repetitive finger movements at four time points: (i) before their usual morning dose of L-DOPA (T0), (ii) 30 min (T1), (iii) 1 h and 30 min (T2), and (iv) 3 h and 30 min after the L-DOPA intake (T3). RESULTS: Movement velocity and amplitude of finger movements were higher in PD patients during the session with OPC compared to the session without OPC at all the time points tested. Importantly, the variability of finger movement velocity and amplitude across T0-T3 was significantly lower in the L-DOPA + OPC than L-DOPA session. CONCLUSIONS: This study is the first objective assessment of the effects of adding OPC to L-DOPA on bradykinesia in patients with PD and motor fluctuations. OPC, in addition to the standard dopaminergic therapy, leads to significant improvements in bradykinesia during clinically relevant periods associated with peripheral L-DOPA dynamics, i.e., the OFF state in the morning, delayed-ON, and wearing-OFF periods.

A pilot study for testing a low-cost 3D design with an inertial sensor for the quantitative assessment of finger tapping in patients with Parkinson's Disease.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders worldwide. Current identification and monitoring of its motor symptoms depends on the clinical expertise. Repetitive finger tapping is one of the most common clinical maneuvers to assess for bradykinesia. Despite the increasing use of technology aids to quantitatively characterize the motor symptoms of PD, there is still a relative lack of clinical evidence to support their widespread use, particularly in low-resource settings. In this pilot study, we used a low-cost design prototype coupled with an inertial sensor is coupled to quantify the frequency of the finger tapping movements in four participants with PD. Repetitive finger tapping was performed using both hands before and after taking levodopa as part of their clinical treatment. The proposed 3D design allowed repetitive movements to be performed without issues. The maximum frequency of finger tapping was in the range of 0.1 to 4.3 Hz. Levodopa was associated with variable changes in the maximum frequency of finger tapping. This pilot study shows the feasibility for low-cost technology to quantitatively characterize repetitive movements in people living with PD.Clinical relevance- In this pilot study, a low-cost inertial sensor coupled to a design prototype was feasible to characterize the frequency of repetitive finger tapping movements in four participants with PD. This method could be used to quantitatively identify and monitor bradykinesia in people living with PD.

Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson's Disease.

The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson's Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson's Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future.

Clinical assessment of upper limb impairments and functional capacity in Parkinson's disease: a systematic review.

BACKGROUND: Parkinson's disease (PD) may progressively reduce the upper limb's functionality. Currently, there is no standardized upper limb functional capacity assessment in PD in the rehabilitation field. OBJECTIVE: To identify specific outcome measurements to assess upper limbs in PD and access functional capacity. METHODS: We systematically reviewed and analyzed the literature in English published from August/2012 to August/2022 according to PRISMA. The following keywords were used in our search: "upper limbs" OR "upper extremity" and "Parkinson's disease." Two researchers searched independently, including studies accordingly to our inclusion and exclusion criteria. Registered at PROSPERO CRD42021254486. RESULTS: We found 797 studies, and 50 were included in this review (n = 2.239 participants in H&Y stage 1-4). The most common upper limbs outcome measures found in the studies were: (i) UPDRS-III and MDS-UPDRS to assess the severity and progression of PD motor symptoms (tremor, bradykinesia, and rigidity) (ii) Nine Hole Peg Test and Purdue Pegboard Test to assess manual dexterity; (iii) Spiral test and Funnel test to provoke and assess freezing of upper limbs; (iv) Technology assessment such as wearables sensors, apps, and other device were also found. CONCLUSION: We found evidence to support upper limb impairments assessments in PD. However, there is still a large shortage of specific tests to assess the functional capacity of the upper limbs. The upper limbs' functional capacity is insufficiently investigated during the clinical and rehabilitation examination due to a lack of specific outcome measures to assess functionality.

ANTECEDENTES: A doença de Parkinson (DP) reduz progressivamente a funcionalidade do membro superior. Não existe uma avaliação padronizada da capacidade funcional do membro superior na DP na área da reabilitação. OBJETIVO: Identificar medidas de resultados específicos para avaliar membros superiores na DP e avaliar capacidade funcional. MéTODOS: Revisamos e analisamos sistematicamente a literatura publicada de agosto/2012 a agosto/2022 de acordo com PRISMA. Usamos as seguintes palavras-chave "membros superiores" OU "extremidade superior" e "doença de Parkinson." Dois pesquisadores fizeram a busca de forma independente, incluindo estudos de acordo com os critérios de inclusão e exclusão. Registro PROSPERO CRD42021254486. RESULTADOS: Encontramos 797 estudos, 50 foram incluídos no estudo(n = 2.239 participantes no estágio 1­4 de H&Y). As medidas de resultados de membros superiores mais comuns encontradas foram: (i) UPDRS-III e MDS-UPDRS, para avaliar a gravidade e a progressão dos sintomas motores da DP (tremor, bradicinesia, e rigidez); (ii) Nine Hole Peg Test e Purdue Pegboard Test para avaliar a destreza manual; (iii) Teste da Espiral e Teste do Funil para provocar e avaliar o congelamento de membros superiores; (iv) Avaliação de tecnologia, como sensores vestíveis, aplicativos e outros dispositivos também foram encontrados. CONCLUSãO: Encontramos evidências para dar suporte para as avaliações de deficiências de membros superiores na DP. No entanto, ainda há grande escassez de testes específicos para avaliar a capacidade funcional dos membros superiores. A capacidade funcional dos membros superior é insuficientemente investigada durante o exame clínico e de reabilitação devido à falta de medidas de resultados específicos para avaliar a funcionalidade.

Objective measurement versus clinician-based assessment for Parkinson's disease.

INTRODUCTION: Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED: The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION: In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.

A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data.

Bradykinesia is a cardinal hallmark of Parkinson's disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson's Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson's disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia.

Bradykinesia assessment in children with cerebral palsy and periventricular leukomalacia.

OBJECTIVE: To analyse the motor phenotype with a focus on bradykinesia in children with Cerebral Palsy (CP) in the setting of periventricular leukomalacia (PVL). METHODOLOGY: Analysis of a cohort of 25 children with CP and PVL. The Gross Motor Function Classification System (GMFCS) and the Manual Ability Classification System (MACS) were used to classify the severity of motor function. Spasticity was rated using the Modified Ashworth Scale (MAS), dystonia was rated using the Burke-Fahn-Marsden Scale (BFMS), and bradykinesia was rated using the Unified Parkinson's disease rating scale (UPDRS). All patients were video-recorded following a standard protocol. RESULTS: Bradykinesia was observed in 96% of patients. It was noted mainly in the limbs, and it was moderate-to-severe in the legs and mild-to-moderate in the arms. Bradykinesia correlated with functional level, as classified by GMFCS and MACS; also with dystonia, as rated by BFMS but did not correlate with a measure of spasticity (MAS). CONCLUSIONS: This study confirms the existence of bradykinesia in patients with CP in the setting of PVL. Bradykinesia and dystonia appear to be important interrelated factors influencing the level of gross and fine motor skills in patients with PVL.

A tree-structure-guided graph convolutional network with contrastive learning for the assessment of parkinsonian hand movements.

Bradykinesia is one of the core motor symptoms of Parkinson's disease (PD). Neurologists typically perform face-to-face bradykinesia assessment in PD patients according to the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). As this human-expert assessment lacks objectivity and consistency, an automated and objective assessment scheme for bradykinesia is critically needed. In this paper, we propose a tree-structure-guided graph convolutional network with contrastive learning scheme to solve the challenge of difficulty in fine-grained feature extraction and insufficient model stability, finally achieving the video-based automated assessment of Parkinsonian hand movements, which represent a vital MDS-UPDRS component for examining upper-limb bradykinesia. Specifically, a tri-directional skeleton tree scheme is proposed to achieve effective fine-grained modeling of spatial hand dependencies. In this scheme, hand skeletons are extracted from videos, and then the spatial structures of these skeletons are constructed through depth-first tree traversal. Afterwards, a tree max-pooling module is employed to establish remote exchange between outer and inner nodes, hierarchically gather the most salient motion features, and hence achieve fine-grained mining. Finally, a group-sparsity-induced momentum contrast is also developed to learn similar motion patterns under different interference through contrastive learning. This can promote stable learning of discriminative spatial-temporal features with invariant motion semantics. Comprehensive experiments on a large clinical video dataset reveal that our method achieves competitive results, and outperforms other sensor-based and RGB-depth methods. The proposed method leads to accurate assessment of PD bradykinesia through videos collected by low-cost consumer cameras of limited capabilities. Hence, our work provides a convenient tool for PD telemedicine applications with modest hardware requirements.

A non-contact system for intraoperative quantitative assessment of bradykinesia in deep brain stimulation surgery.

BACKGROUND AND OBJECTIVE: Deep brain stimulation (DBS) is an effective treatment for a number of neurological diseases, especially for the advanced stage of Parkinson's disease (PD). Objective assessment of patients' motor symptoms is crucial for accurate electrode targeting and treatment. Existing approaches suffer from subjective variability or interference with voluntary motion. This work is aimed to establish an objective assessment system to quantify bradykinesia in DBS surgery. METHODS: Based on the analysis of the requirements for intraoperative assessment, we developed a system with non-contact measurement, online movement feature extraction, and interactive data analysis and visualization. An optical sensor, Leap Motion Controller (LMC), was taken to detect hand movement in three clinical tasks. A graphic user interface was designed to process, compare and visualize the collected data and assessment results online. Quantified movement features include amplitude, frequency, velocity, their decrement and variability, etc. Technical validation of the system was performed with a motion capture system (Mocap), with respect to data-level and feature-level accuracy and reliability. Clinical validation was conducted with 20 PD patients for intraoperative assessments in DBS surgery. Treatment responses with respect to the bradykinesia movement features were analyzed. Single case analysis and group statistical analysis were performed to examine the differences between preoperative and intraoperative performance, and the correlation between the clinical ratings and the quantified assessment was analyzed. RESULTS: For the movements measured by LMC and Mocap, the average Pearson's correlation coefficient was 0.986, and the mean amplitude difference was 2.11 mm. No significant difference was found for all movement features quantified by LMC and Mocap. For the clinical tests, key movement features showed significant differences between the preoperative baseline and intraoperative performance when the brain stimulation was ON. The assessment results were significantly correlated with the MDS-UPDRS clinical ratings. CONCLUSIONS: The proposed non-contact system has established itself as an objective intraoperative assessment, analysis, and visualization tool for DBS treatment of Parkinson's disease.

Video-Based Automated Assessment of Movement Parameters Consistent with MDS-UPDRS III in Parkinson's Disease.

BACKGROUND: Among motor symptoms of Parkinson's disease (PD), including rigidity and resting tremor, bradykinesia is a mandatory feature to define the parkinsonian syndrome. MDS-UPDRS III is the worldwide reference scale to evaluate the parkinsonian motor impairment, especially bradykinesia. However, MDS-UPDRS III is an agent-based score making reproducible measurements and follow-up challenging. OBJECTIVE: Using a deep learning approach, we developed a tool to compute an objective score of bradykinesia based on the guidelines of the gold-standard MDS-UPDRS III. METHODS: We adapted and applied two deep learning algorithms to detect a two-dimensional (2D) skeleton of the hand composed of 21 predefined points, and transposed it into a three-dimensional (3D) skeleton for a large database of videos of parkinsonian patients performing MDS-UPDRS III protocols acquired in the Movement Disorder unit of Avicenne University Hospital. RESULTS: We developed a 2D and 3D automated analysis tool to study the evolution of several key parameters during the protocol repetitions of the MDS-UPDRS III. Scores from 2D automated analysis showed a significant correlation with gold-standard ratings of MDS-UPDRS III, measured with coefficients of determination for the tapping (0.609) and hand movements (0.701) protocols using decision tree algorithms. The individual correlations of the different parameters measured with MDS-UPDRS III scores carry meaningful information and are consistent with MDS-UPDRS III guidelines. CONCLUSION: We developed a deep learning-based tool to precisely analyze movement parameters allowing to reliably score bradykinesia for parkinsonian patients in a MDS-UPDRS manner.

Assessment of Parkinson's Disease Severity From Videos Using Deep Architectures.

Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism. Seven tasks from the Movement Disorders Society - Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification.

Parkinson's disease medication state and severity assessment based on coordination during walking.

Walking is a complex motor function requiring coordination of all body parts. Parkinson's disease (PD) motor signs such as rigidity, bradykinesia, and impaired balance affect movements including walking. Here, we propose a computational method to objectively assess the effects of Parkinson's disease pathology on coordination between trunk, shoulder and limbs during the gait cycle to assess medication state and disease severity. Movements during a scripted walking task were extracted from wearable devices placed at six different body locations in participants with PD and healthy participants. Three-axis accelerometer data from each device was synchronized at the beginning of either left or right steps. Canonical templates of movements were then extracted from each body location. Movements projected on those templates created a reduced dimensionality space, where complex movements are represented as discrete values. These projections enabled us to relate the body coordination in people with PD to disease severity. Our results show that the velocity profile of the right wrist and right foot during right steps correlated with the participant's total score on the gold standard Unified Parkinson's Disease Rating Scale (UPRDS) with an r2 up to 0.46. Left-right symmetry of feet, trunk and wrists also correlated with the total UPDRS score with an r2 up to 0.3. In addition, we demonstrate that binary dopamine replacement therapy medication states (self-reported 'ON' or 'OFF') can be discriminated in PD participants. In conclusion, we showed that during walking, the movement of body parts individually and in coordination with one another changes in predictable ways that vary with disease severity and medication state.

Assessment of Wearing Off in Parkinson's disease using objective measurement.

BACKGROUND: Development of "Wearing Off" (WO) of motor and non-motor function in Parkinson's disease (PD) adversely affects quality of life. This suggest that identifying and treating WO is important. However, identification of WO depends on people with PD (PwP) recognising and reporting WO and there is a perception that WO may be significantly underestimated. OBJECTIVE: We investigate the feasibility of identifying "Wearing Off" using objective measurement and assess the clinical benefit in rectifying it. METHOD: In this study, 200 PwP were studied for evidence of WO using a continuously worn wearable system. Eighty-five patients (43%) were found to have WO and treatment was changed to mitigate the effects of WO. RESULTS: Factors, such as duration of disease, high baseline MDS-UPDRS (motor component), high Percent Time in Bradykinesia (PTB), high Levodopa Equivalent Daily Dose (LEDD), frequent Levodopa doses and younger age of onset, are associated with severity of motor complications. Patients with more severe WO experienced worse motor and non-motor symptoms and lower quality of life. Quality of life significantly improved in PwP when WO was treated. CONCLUSION: The findings reported in this study provide evidence that identifying and treating WO improves outcomes of PwP and that objective measurements may help clinicians to identify and treat WO.

Sensor-Based and Patient-Based Assessment of Daily-Living Physical Activity in People with Parkinson's Disease: Do Motor Subtypes Play a Role?

The benefits of daily-living physical activity are clear. Nonetheless, the relationship between physical activity levels and motor subtypes of Parkinson's disease (PD), i.e., tremor dominant (TD) and postural instability gait difficulty (PIGD), have not been well-studied. It is also unclear if patient perspectives and motor symptom severity are related to objective, sensor-based assessment of daily-living activity in those subtypes. To address these questions, total daily-living physical activity was quantified in 73 patients with PD and 29 healthy controls using a 3D-accelerometer worn on the lower back for at least three days. We found that individuals with the PIGD subtype were significantly less active than healthy older adults (p = 0.007), unlike individuals with the TD subtype. Among the PIGD subtype, higher daily physical activity was negatively associated with more severe ON bradykinesia (rS = -0.499, p = 0.002), motor symptoms (higher ON MDS-UPDRS (Unified Parkinson's Disease Rating Scale motor examination)-III scores), gait difficulties (rS = -0.502, p = 0.002), motor complications (rS = 0.466, p = 0.004), and balance (rS = 0.519, p = 0.001). In contrast, among the TD subtype, disease-related characteristics were not related to daily-living physical activity. Intriguingly, physical activity was not related to self-report of ADL difficulties (scores of the MDS-UPDRS Parts I or II) in both motor subtypes. These findings highlight the importance of objective daily-living physical activity monitoring and suggest that self-report does not necessarily reflect objective physical activity levels. Furthermore, the results point to important differences in factors related to physical activity in PD motor subtypes, setting the stage for personalized treatment programs.

Instrumental Assessment of Stepping in Place Captures Clinically Relevant Motor Symptoms of Parkinson's Disease.

Fluctuations of motor symptoms make clinical assessment in Parkinson's disease a complex task. New technologies aim to quantify motor symptoms, and their remote application holds potential for a closer monitoring of treatment effects. The focus of this study was to explore the potential of a stepping in place task using RGB-Depth (RGBD) camera technology to assess motor symptoms of people with Parkinson's disease. In total, 25 persons performed a 40 s stepping in place task in front of a single RGBD camera (Kinect for Xbox One) in up to two different therapeutic states. Eight kinematic parameters were derived from knee movements to describe features of hypokinesia, asymmetry, and arrhythmicity of stepping. To explore their potential clinical utility, these parameters were analyzed for their Spearman's Rho rank correlation to clinical ratings, and for intraindividual changes between treatment conditions using standard response mean and paired t-test. Test performance not only differed between ON and OFF treatment conditions, but showed moderate correlations to clinical ratings, specifically ratings of postural instability (pull test). Furthermore, the test elicited freezing in some subjects. Results suggest that this single standardized motor task is a promising candidate to assess an array of relevant motor symptoms of Parkinson's disease. The simple technical test setup would allow future use by patients themselves.

Automatic detection and quantification of hand movements toward development of an objective assessment of tremor and bradykinesia in Parkinson's disease.

BACKGROUND: Classification of parkinsonian symptoms, including tremor and bradykinesia, require the application of validated clinical rating scales which are inherently subjective. In this study, we assessed an objective measure of parkinsonian symptomology using automated analysis of hand gestures. NEW METHOD: We constructed and evaluated a hand and finger motion capture apparatus and analysis pipeline that recorded hand/finger motion of control subjects and patients with Parkinson's disease. The detailed three-dimensional (3D) motion features of each finger joint was extracted by using Discrete Wavelet Transform (DWT). The severity of tremor for each finger joint was quantitated by analyzing the motion changes in the frequency domain on four types of motion from five patients and twenty-two control subjects. RESULTS: The proposed approach could distinguish the behavior of patients with Parkinson's disease and control subjects by analyzing the detailed motion features of their hands/fingers. COMPARISON WITH EXISTING METHODS: Previously established methods to quantitate finger movement dynamics focus on speed and amplitude. In contrast, our approach measures unsupervised motion features, in real-time, using wavelet analysis, of each individual finger joint during active free movement. CONCLUSIONS: The proposed study provides an objective assessment of tremor and bradykinesia in Parkinson's disease. Accordingly, this may help movement disorder clinicians to detect, diagnose and monitor treatment efficacy in Parkinson's disease.

New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease.

Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.