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.

Time-resolved quantification of fine hand movements as a proxy for evaluating bradykinesia-induced motor dysfunction.

Bradykinesia is a behavioral manifestation that contributes to functional dependencies in later life. However, the current state of bradykinesia indexing primarily relies on subjective, time-averaged categorizations of motor deficits, which often yield poor reliability. Herein, we used time-resolved analyses of accelerometer recordings during standardized movements, data-driven factor analyses, and linear mixed effects models (LMEs) to quantitatively characterize general, task- and therapy-specific indices of motor impairment in people with Parkinson's disease (PwP) currently undergoing treatment for bradykinesia. Our results demonstrate that single-trial, accelerometer-based features of finger-tapping and rotational hand movements were significantly modulated by divergent therapeutic regimens. Further, these features corresponded well to current gold standards for symptom monitoring, with more precise predictive capacities of bradykinesia-specific declines achieved when considering kinematic features from diverse movement types together, rather than in isolation. Herein, we report data-driven, sample-specific kinematic profiles of diverse movement types along a continuous spectrum of motor impairment, which importantly, preserves the temporal scale for which biomechanical fluctuations in motor deficits evolve in humans. Therefore, this approach may prove useful for tracking bradykinesia-induced motor decline in aging populations the future.

The sound of Parkinson's disease: A model of audible bradykinesia.

INTRODUCTION: Evaluation of bradykinesia is based on five motor tasks from the MDS-UPDRS. Visually scoring these motor tasks is subjective, resulting in significant interrater variability. Recent observations suggest that it may be easier to hear the characteristic features of bradykinesia, such as the decrement in sound intensity or force of repetitive movements. The objective is to evaluate whether audio signals derived during four MDS-UPDRS tasks can be used to detect and grade bradykinesia, using two machine learning models. METHODS: 54 patients with Parkinson's disease and 28 healthy controls were filmed while executing the bradykinesia motor tasks. Several features were extracted from the audio signal, including number of taps, speed, sound intensity, decrement and freezes. For each motor task, two supervised machine learning models were trained, Logistic Regression (LR) and Support Vector Machine (SVM). RESULTS: Both classifiers were able to separate patients from controls reasonably well for the leg agility task, area under the receiver operating characteristic curve (AUC): 0.92 (95%CI: 0.78-0.99) for LR and 0.93 (0.81-1.00) for SVM. Also, models were able to differentiate less severe bradykinesia from severe bradykinesia, particularly for the pronation-supination motor task, with AUC: 0.90 (0.62-1.00) for LR and 0.82 (0.45-0.97) for SVM. CONCLUSION: This audio-based approach discriminates PD from healthy controls with moderate-high accuracy and separated individuals with less severe bradykinesia from those with severe bradykinesia. Sound analysis may contribute to the identification and monitoring of bradykinesia.

A Deep Learning Approach for Grading of Motor Impairment Severity in Parkinson's Disease.

Objective and quantitative monitoring of movement impairments is crucial for detecting progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. A convolutional neural network architecture, XceptionTime, was used to classify lower and higher levels of motor impairment in persons with PD, across five distinct rhythmic tasks: finger tapping, hand movements, pronation-supination movements of the hands, toe tapping, and leg agility. In addition, an aggregate model was trained on data from all tasks together for evaluating bradykinesia symptom severity in PD. The model performance was highest in the hand movement tasks with an accuracy of 82.6% in the hold-out test dataset; the accuracy for the aggregate model was 79.7%, however, it demonstrated the lowest variability. Overall, these findings suggest the feasibility of integrating low-cost wearable technology and deep learning approaches to automatically and objectively quantify motor impairment in persons with PD. This approach may provide a viable solution for a widely deployable telemedicine solution.

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.

May Bradykinesia Features Aid in Distinguishing Parkinson's Disease, Essential Tremor, And Healthy Elderly Individuals?

BACKGROUND: Bradykinesia is the hallmark feature of Parkinson's disease (PD); however, it can manifest in other conditions, including essential tremor (ET), and in healthy elderly individuals. OBJECTIVE: Here we assessed whether bradykinesia features aid in distinguishing PD, ET, and healthy elderly individuals. METHODS: We conducted simultaneous video and kinematic recordings of finger tapping in 44 PD patients, 69 ET patients, and 77 healthy elderly individuals. Videos were evaluated blindly by expert neurologists. Kinematic recordings were blindly analyzed. We calculated the inter-raters agreement and compared data among groups. Density plots assessed the overlapping in the distribution of kinematic data. Regression analyses and receiver operating characteristic curves determined how the kinematics influenced the likelihood of belonging to a clinical score category and diagnostic group. RESULTS: The inter-rater agreement was fair (Fleiss K = 0.32). Rater found the highest clinical scores in PD, and higher scores in ET than healthy elderly individuals (p < 0.001). In regard to kinematic analysis, the groups showed variations in movement velocity, with PD presenting the slowest values and ET displaying less velocity than healthy elderly individuals (all ps < 0.001). Additionally, PD patients showed irregular rhythm and sequence effect. However, kinematic data significantly overlapped. Regression analyses showed that kinematic analysis had high specificity in differentiating between PD and healthy elderly individuals. Nonetheless, accuracy decreased when evaluating subjects with intermediate kinematic values, i.e., ET patients. CONCLUSION: Despite a considerable degree of overlap, bradykinesia features vary to some extent in PD, ET, and healthy elderly individuals. Our findings have implications for defining bradykinesia and categorizing patients.

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.

Parkinsonian Hand or Clinician's Eye? Finger Tap Bradykinesia Interrater Reliability for 21 Movement Disorder Experts.

BACKGROUND: Bradykinesia is considered the fundamental motor feature of Parkinson's disease (PD). It is central to diagnosis, monitoring, and research outcomes. However, as a clinical sign determined purely by visual judgement, the reliability of humans to detect and measure bradykinesia remains unclear. OBJECTIVE: To establish interrater reliability for expert neurologists assessing bradykinesia during the finger tapping test, without cues from additional examination or history. METHODS: 21 movement disorder neurologists rated finger tapping bradykinesia, by Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Modified Bradykinesia Rating Scale (MBRS), in 133 videos of hands: 73 from 39 people with idiopathic PD, 60 from 30 healthy controls. Each neurologist rated 30 randomly-selected videos. 19 neurologists were also asked to judge whether the hand was PD or control. We calculated intraclass correlation coefficients (ICC) for absolute agreement and consistency of MDS-UPDRS ratings, using standard linear and cumulative linked mixed models. RESULTS: There was only moderate agreement for finger tapping MDS-UPDRS between neurologists, ICC 0.53 (standard linear model) and 0.65 (cumulative linked mixed model). Among control videos, 53% were rated > 0 by MDS-UPDRS, and 24% were rated as bradykinesia by MBRS subscore combination. Neurologists correctly identified PD/control status in 70% of videos, without strictly following bradykinesia presence/absence. CONCLUSION: Even experts show considerable disagreement about the level of bradykinesia on finger tapping, and frequently see bradykinesia in the hands of those without neurological disease. Bradykinesia is to some extent a phenomenon in the eye of the clinician rather than simply the hand of the person with PD.

Diagnosis of vascular parkinsonism: A new tool for gait hypokinesia occurring in older persons.

INTRODUCTION: Reliable diagnosis of vascular parkinsonism (VaP) in the presence of a gait hypokinesia is an issue that is encountered in geriatrics. The EVAMAR-AGEX study was focusing on the phenomenon of recurrent falls in older persons (OP) with this parkinsonian gait. The present study is focusing on the diagnosis of VaP-related parkinsonian gait by developing a diagnostic guidance model adapted to OP. METHODS: Data from baseline and the 2-year follow-up visit were used to carry out univariate analysis and calculation of odds ratios, allowing to identify relevant variables to include in the diagnostic guidance model. To evaluate the model, confusion matrices were created, evaluating true positive, false negative, false positive and true negative incidences, sensitivity and specificity, and negative and positive predictive values. RESULTS: 79 patients included 58% male; average age 81.24 years. VaP diagnosis according to Zijlmans criteria occurred in 28%; neurodegenerative parkinsonian syndromes in 72%. A 4-criteria model was established to facilitate diagnostic: lack of prior hallucinations, lack of movement disorders tremor excluded, no cognitive fluctuations, and ≥75 years of age at diagnosis. In combination of 4/4 criteria, all of them were required to disclose a specificity of 91% in the diagnosis of VaP. In combination of 3/4, in case of negative test, a negative predictive value for VaP diagnosis of 0.97 was obtained. CONCLUSION: The challenge of our tool is both to be able to rule out what is probably not a VaP and to argue what makes a VaP diagnosis probable in OP.

The use of wearables for the diagnosis and treatment of Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient's medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population.

A systematic review of local field potential physiomarkers in Parkinson's disease: from clinical correlations to adaptive deep brain stimulation algorithms.

Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson's disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of 'physiomarkers' extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.

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.

Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors.

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.

Flexible Forearm Triboelectric Sensors for Parkinson's Disease Diagnosing and Monitoring.

Existing approaches that assess and monitor the severity of Parkinson's Disease (PD) focus on the integration of wearable devices based on inertial sensors (accelerometers, gyroscopes) and electromyographic (EMG) transducers. Nevertheless, some of these sensors are bulky and lack comfortability. This manuscript presents triboelectric nanogenerators (TENGs) as an alternative stretchable sensor solution enabling PD monitoring systems. The prototype has been developed using a triboelectric sensor based on Ecoflex™ and PEDOT:PSS that is placed on the forearm. The movement of the skin above the forearm muscles and tendons correlates with the extension and flexion of fingers and hands. This way, the small gap of 0.5 cm between the polymer layers is displaced, generating voltage due to the triboelectric contact. Signals from preliminary experiments can discriminate different dynamics of emulated tremor and bradykinesia in hands and fingers. A modified version of the TS is integrated with a printed circuit board (PCB) in a single package with signal conditioning and wireless data transmission. The sensor platforms have demonstrated a good sensitivity to PD symptoms like bradykinesia and tremor based on the Unified Parkinson's Disease Rating Scale (MDS:UPDRS).

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.

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.

Impaired bed mobility in prediagnostic and de novo Parkinson's disease.

BACKGROUND: Wearable technology research suggests that nocturnal movements are disturbed in early Parkinson's disease (PD). In this study, we investigate if patients also already experience impaired bed mobility before PD diagnosis. Furthermore, we explore its association with motor and nonmotor features and its value for phenoconversion and disease progression prediction. METHODS: PPMI data were downloaded for de novo PD subjects, subjects at-risk for developing a synucleinopathy (with isolated REM sleep behavior disorder, hyposmia or a pathogenic genetic variant) and controls. Impaired bed mobility was assessed with the MDS-UPDRS part 2 item 9. A frequency analysis was performed. Multivariable logistic regression analyses were used to investigate the association with other PD variables. Cox proportional-hazards models were used to test if difficulties with turning in bed could predict phenoconversion. Linear mixed models were used to evaluate if difficulties with turning in bed could predict disease progression. RESULTS: Of the at-risk subjects, 9.2-12.5% experienced difficulties with turning in bed vs. 25.0% of de novo PD subjects and 2.5% of controls. Impaired turning ability was associated with MDS-UPDRS motorscore (axial signs in the at-risk group, bradykinesia in the de novo PD group) and SCOPA-AUT score (gastrointestinal symptoms). In addition, difficulties with turning in bed were a significant predictor for phenoconversion in the at-risk group and for development of motor complications in the de novo PD group. CONCLUSION: Our findings suggest that difficulties with turning in bed can be helpful as clinical symptom for a prodromal PD screening and for motor complication prediction in early PD.

Development and validation of an instrument for measuring parkinsonian motor impairment: TRAPS-D.

BACKGROUND: Parkinson's disease is incurable, idiopathic, degenerative, and progressive, and affects about 1% of the elderly population. Multidisciplinary clinical treatment is the best and most adopted therapeutic option, while surgical treatment is used in less than 15% of those affected. In practice, there is a lack of reliable and validated scales for measuring motor impairment, and monitoring and screening for surgical indications. OBJECTIVE: To develop and validate an instrument for measuring parkinsonian motor impairment in candidates for neurosurgical treatment. METHOD: The development and validation methods followed published guidelines. The first part was the choice of domains that would make up the construct: cardinal signs of disease (tremor, rigidity (stiffness), posture/balance/gait, hypokinesia/akinesia, and speech), along with pain and dyskinesia. A multi-professional working group prepared an initial pilot instrument. Ten renowned specialists evaluated, judged, and suggested modifications to the instrument. The second phase was the evaluation of the content of each domain and the respective ability to classify commitment intensity. The third phase was the correction of the main flaws detected and new submission to the board. The instrument was applied to 41 candidates for neurosurgical treatment in two situations: with and without medication RESULTS: The final form received 100% agreement from the judges. Its average time for application was 8 min. It was very responsive (p = 0.001, Wilcoxon) in different situations (On-Off). CONCLUSION: TRASP-D is a valid instrument for measuring motor impairment in patients with Parkinson's disease who are candidates for neurosurgical treatment. It allows measurement in multiple domains with reliability and sensitivity.