Technologies Assessing Limb Bradykinesia in Parkinson's Disease

Bradykinesia in dystonic hand tremor: kinematic analysis and clinical rating

Introduction

Bradykinesia is an essential diagnostic criterion for Parkinson's disease (PD) but is frequently observed in many non-parkinsonian movement disorders, complicating differential diagnosis, particularly in disorders featuring tremors. The presence of bradykinetic features in the subset of dystonic tremors (DT), either "pure" dystonic tremors or tremors associated with dystonia, remains currently unexplored. The aim of the current study was to evaluate upper limb bradykinesia in DT patients, comparing them with healthy controls (HC) and patients with PD by observing repetitive finger tapping (FT).

Methods

The protocol consisted of two main parts. Initially, the kinematic recording of repetitive FT was performed using optical hand tracking system (Leap Motion Controller). The values of amplitude, amplitude decrement, frequency, frequency decrement, speed, acceleration and number of halts of FT were calculated. Subsequently, three independent movement disorder specialists from different movement disorders centres, blinded to the diagnosis, rated the presence of FT bradykinesia based on video recordings.

Results

Thirty-six subjects participated in the study (12 DT, 12 HC and 12 early-stage PD). Kinematic analysis revealed no significant difference in the selected parameters of FT bradykinesia between DT patients and HC. In comparisons between DT and PD patients, PD patients exhibited bigger amplitude decrement and slower FT performance. In the blinded clinical assessment, bradykinesia was rated, on average, as being present in 41.6% of DT patients, 27.7% of HC, and 91.7% of PD patients. While overall inter-rater agreement was moderate, weak agreement was noted within the DT group.

Discussion

Clinical ratings indicated signs of bradykinesia in almost half of DT patients. The objective kinematic analysis confirmed comparable parameters between DT and HC individuals, with more pronounced abnormalities in PD across various kinematic parameters. Interpretation of bradykinesia signs in tremor patients with DT should be approached cautiously and objective motion analysis might complement the diagnostic process and serve as a decision support system in the choice of clinical entities.

Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data

X-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based analyses in XDP participants to quantitatively characterize disease phenomenology as a potential clinical trial endpoint. Wearable sensor data was collected from 10 symptomatic XDP patients and 3 healthy controls during a standardized examination. Disease severity was assessed with the Unified Parkinson's Disease Rating Scale Part 3 (MDS-UPDRS) and Burke-Fahn-Marsden dystonia scale (BFM). We collected sensor data during the performance of specific MDS-UPDRS/BFM upper- and lower-limb motor tasks, and derived data features suitable to estimate clinical scores using machine learning (ML). XDP patients were at varying stages of disease and clinical severity. ML-based algorithms estimated MDS-UPDRS scores (parkinsonism) and dystonia-specific data features with a high degree of accuracy. Gait spatio-temporal parameters had high discriminatory power in differentiating XDP patients with different MDS-UPDRS scores from controls, XDP freezing of gait, and dystonic/non-dystonic gait. These analyses suggest the feasibility of using wearable sensor data for deriving reliable clinical score estimates associated with both parkinsonian and dystonic features in a complex, combined movement disorder and the utility of motion sensors in quantifying clinical examination.

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson's disease in nursing homes

Introduction

In the advanced stages of Parkinson's disease (PD), motor complications such as wearing-off and dyskinesia are problematic and vary daily. These symptoms need to be monitored precisely to provide adequate care for patients with advanced PD.

Methods

This study used wearable devices to explore biomarkers for motor complications by measuring multiple biomarkers in patients with PD residing in facilities and combining them with lifestyle and clinical assessments. Data on the pulse rate and activity index (metabolic equivalents) were collected from 12 patients over 30 days.

Results

The pulse rate and activity index during the off- and on-periods and dyskinesia were analyzed for two participants; the pulse rate and activity index did not show any particular trend in each participant; however, the pulse rate/activity index was significantly greater in the off-state compared to that in the dyskinesia and on-states, and this index in the dyskinesia state was significantly greater than that in the on-state in both participants.

Conclusion

These results suggest the pulse rate and activity index combination would be a useful indicator of wearing-off and dyskinesia and that biometric information from wearable devices may function as a digital diary. Accumulating more cases and collecting additional data are necessary to verify our findings.

A smartphone-based tapping task as a marker of medication response in Parkinson's disease: a proof of concept study

Tapping tasks have the potential to distinguish between ON-OFF fluctuations in Parkinson's disease (PD) possibly aiding assessment of medication status in e-diaries and research. This proof of concept study aims to assess the feasibility and accuracy of a smartphone-based tapping task (developed as part of the cloudUPDRS-project) to discriminate between ON-OFF used in the home setting without supervision. 32 PD patients performed the task before their first medication intake, followed by two test sessions after 1 and 3 h. Testing was repeated for 7 days. Index finger tapping between two targets was performed as fast as possible with each hand. Self-reported ON-OFF status was also indicated. Reminders were sent for testing and medication intake. We studied task compliance, objective performance (frequency and inter-tap distance), classification accuracy and repeatability of tapping. Average compliance was 97.0% (± 3.3%), but 16 patients (50%) needed remote assistance. Self-reported ON-OFF scores and objective tapping were worse pre versus post medication intake (p < 0.0005). Repeated tests showed good to excellent test-retest reliability in ON (0.707 ≤ ICC ≤ 0.975). Although 7 days learning effects were apparent, ON-OFF differences remained. Discriminative accuracy for ON-OFF was particularly good for right-hand tapping (0.72 ≤ AUC ≤ 0.80). Medication dose was associated with ON-OFF tapping changes. Unsupervised tapping tests performed on a smartphone have the potential to classify ON-OFF fluctuations in the home setting, despite some learning and time effects. Replication of these results are needed in a wider sample of patients.

Exploring the Complex Phenotypes of Impaired Finger Dexterity in Mild-to-moderate Stage Parkinson's Disease: A Time-Series Analysis

BACKGROUND

Impaired dexterity is an early motor symptom in Parkinson's disease (PD) that significantly impacts the daily activity of patients; however, what constitutes complex dexterous movements remains controversial.

OBJECTIVE

To explore the characteristics of finger dexterity in mild-to-moderate stage PD.

METHODS

We quantitatively assessed finger dexterity in 48 mild-to-moderate stage PD patients and 49 age-matched controls using a simple alternating two-finger typing test for 15 seconds. Time-series analyses of various kinematic parameters with machine learning were compared between sides and groups.

RESULTS

Both the more and less affected hands of patients with PD had significantly lower typing frequency and slower typing velocity than the non-dominant and the dominant hands of controls (p = 0.019, p = 0.016, p < 0.001, p < 0.001). The slope of the typing velocity decreased with time, indicating a sequence effect in the PD group. A typing duration of 6 seconds was determined sufficient to discriminate PD patients from controls. Typing error, repetition, and repetition rate were significantly higher in the more affected hands of patients with PD than in the non-dominant hand of controls (p < 0.001, p = 0.03, p < 0.001). The error rate was constant, whereas the repetition rate was steep during the initiation of typing. A predictive model of the more affected hand demonstrated an accuracy of 70% in differentiating PD patients from controls.

CONCLUSION

Our study demonstrated complex components of impaired finger dexterity in mild-to-moderate stage PD, namely bradykinesia with sequence effects, error, and repetition at the initiation of movement, suggesting that multiple neural networks may be involved in dexterity deficits in PD.

Kinect-based objective evaluation of bradykinesia in patients with Parkinson's disease

Objective

To quantify bradykinesia in Parkinson's disease (PD) with a Kinect depth camera-based motion analysis system and to compare PD and healthy control (HC) subjects.

Methods

Fifty PD patients and twenty-five HCs were recruited. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was used to evaluate the motor symptoms of PD. Kinematic features of five bradykinesia-related motor tasks were collected using Kinect depth camera. Then, kinematic features were correlated with the clinical scales and compared between groups.

Results

Significant correlations were found between kinematic features and clinical scales (P < 0.05). Compared with HCs, PD patients exhibited a significant decrease in the frequency of finger tapping (P < 0.001), hand movement (P < 0.001), hand pronation-supination movements (P = 0.005), and leg agility (P = 0.003). Meanwhile, PD patients had a significant decrease in the speed of hand movements (P = 0.003) and toe tapping (P < 0.001) compared with HCs. Several kinematic features exhibited potential diagnostic value in distinguishing PD from HCs with area under the curve (AUC) ranging from 0.684-0.894 (P < 0.05). Furthermore, the combination of motor tasks exhibited the best diagnostic value with the highest AUC of 0.955 (95% CI = 0.913-0.997, P < 0.001).

Conclusion

The Kinect-based motion analysis system can be applied to evaluate bradykinesia in PD. Kinematic features can be used to differentiate PD patients from HCs and combining kinematic features from different motor tasks can significantly improve the diagnostic value.

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.

Clinical neurophysiology of Parkinson's disease and parkinsonism

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This review is part of the series on the clinical neurophysiology of movement disorders and focuses on Parkinson’s disease and parkinsonism.

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The pathophysiology of cardinal parkinsonian motor symptoms and myoclonus are reviewed.

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The recordings from microelectrode and deep brain stimulation electrodes are reported in detail.

This review is part of the series on the clinical neurophysiology of movement disorders. It focuses on Parkinson’s disease and parkinsonism. The topics covered include the pathophysiology of tremor, rigidity and bradykinesia, balance and gait disturbance and myoclonus in Parkinson’s disease. The use of electroencephalography, electromyography, long latency reflexes, cutaneous silent period, studies of cortical excitability with single and paired transcranial magnetic stimulation, studies of plasticity, intraoperative microelectrode recordings and recording of local field potentials from deep brain stimulation, and electrocorticography are also reviewed. In addition to advancing knowledge of pathophysiology, neurophysiological studies can be useful in refining the diagnosis, localization of surgical targets, and help to develop novel therapies for Parkinson’s disease.

A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease

Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.

Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson's disease

Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson’s disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test–retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society–Unified Parkinson's Disease Rating Scale items (rho: 0.12–0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.

Validation of the PD home diary for assessment of motor fluctuations in advanced Parkinson's disease

The Parkinson's disease (PD) home diary is frequently used in clinical trials to measure efficacy of medical treatments for motor fluctuations in advanced PD. This prospective study in fluctuating PD patients examines the validity of the diary for quantification of motor states in comparison to direct clinical observation. 51 patients (median age: 65 years, disease duration: 11 years) completed the diary half-hourly for two consecutive days and were simultaneously rated by an experienced observer, who independently evaluated motor states half-hourly throughout daytime. Overall agreement (Cohen's kappa) between patient and observer diary entries was 59.8% (0.387). Patients documented more On without dyskinesia (52.3% vs. 38.9%, P < 0.001) and less On with dyskinesia (21.5% vs. 34.2%, P < 0.001), whereas proportions for Off intervals were not different between patient and observer diaries (26.2% vs. 27.0%, P = 0.97). Temporal agreement between diary ratings was unsatisfactory, particularly for On with dyskinesia. Taken together, our study suggests that the PD home diary only inadequately reflects actual motor states compared to direct clinical observation.

Developing and assessing a new web-based tapping test for measuring distal movement in Parkinson's disease: a Distal Finger Tapping test

Disability in Parkinson's disease (PD) is measured by standardised scales including the MDS-UPDRS, which are subject to high inter and intra-rater variability and fail to capture subtle motor impairment. The BRadykinesia Akinesia INcoordination (BRAIN) test is a validated keyboard tapping test, evaluating proximal upper-limb motor impairment. Here, a new Distal Finger Tapping (DFT) test was developed to assess distal upper-limb function. Kinetic parameters of the test include kinesia score (KS20, key taps over 20 s), akinesia time (AT20, mean dwell-time on each key) and incoordination score (IS20, variance of travelling time between key taps). To develop and evaluate a new keyboard-tapping test for objective and remote distal motor function in PD patients. The DFT and BRAIN tests were assessed in 55 PD patients and 65 controls. Test scores were compared between groups and correlated with the MDS-UPDRS-III finger tapping sub-scores. Nine additional PD patients were recruited for monitoring motor fluctuations. All three parameters discriminated effectively between PD patients and controls, with KS20 performing best, yielding 79% sensitivity for 85% specificity; area under the receiver operating characteristic curve (AUC) = 0.90. A combination of DFT and BRAIN tests improved discrimination (AUC = 0.95). Among three parameters, KS20 showed a moderate correlation with the MDS-UPDRS finger-tapping sub-score (Pearson's r = - 0.40, p = 0.002). Further, the DFT test detected subtle changes in motor fluctuation states which were not reflected clearly by the MDS-UPDRS-III finger tapping sub-scores. The DFT test is an online tool for assessing distal movements in PD, with future scope for longitudinal monitoring of motor complications.

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.

Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects almost 2% of the population above the age of 65. To better quantify the effects of new medications, fast and objective methods are needed. Touchscreen-based tapping tasks are simple yet effective tools for quantifying drug effects on PD-related motor symptoms, especially bradykinesia. However, there is no consensus on the optimal task set-up. The present study compares four tapping tasks in 14 healthy participants. In alternate finger tapping (AFT), tapping occurred with the index and middle finger with 2.5 cm between targets, whereas in alternate side tapping (AST) the index finger with 20 cm between targets was used. Both configurations were tested with or without the presence of a visual cue. Moreover, for each tapping task, within- and between-day repeatability and (potential) sensitivity of the calculated parameters were assessed. Visual cueing reduced tapping speed and rhythm, and improved accuracy. This effect was most pronounced for AST. On average, AST had a lower tapping speed with impaired accuracy and improved rhythm compared to AFT. Of all parameters, the total number of taps and mean spatial error had the highest repeatability and sensitivity. The findings suggest against the use of visual cueing because it is crucial that parameters can vary freely to accurately capture medication effects. The choice for AFT or AST depends on the research question, as these tasks assess different aspects of movement. These results encourage further validation of non-cued AFT and AST in PD patients.

Slow Motion Analysis of Repetitive Tapping (SMART) Test: Measuring Bradykinesia in Recently Diagnosed Parkinson's Disease and Idiopathic Anosmia

BACKGROUND

Bradykinesia is the defining motor feature of Parkinson's disease (PD). There are limitations to its assessment using standard clinical rating scales, especially in the early stages of PD when a floor effect may be observed.

OBJECTIVE

To develop a quantitative method to track repetitive tapping movements and to compare people in the early stages of PD, healthy controls, and individuals with idiopathic anosmia.

METHODS

This was a cross-sectional study of 99 participants (early-stage PD = 26, controls = 64, idiopathic anosmia = 9). For each participant, repetitive finger tapping was recorded over 20 seconds using a smartphone at 240 frames per second. From each video, amplitude between fingers, frequency (number of taps per second), and velocity (distance travelled per second) was extracted. Clinical assessment was based on the motor section of the MDS-UPDRS.

RESULTS

People in the early stage of PD performed the task with slower velocity (p <  0.001) and with greater frequency slope than controls (p = 0.003). The combination of reduced velocity and greater frequency slope obtained the best accuracy to separate early-stage PD from controls based on metric thresholds alone (AUC = 0.88). Individuals with anosmia exhibited slower velocity (p = 0.001) and smaller amplitude (p <  0.001) compared with controls.

CONCLUSION

We present a simple, proof-of-concept method to detect early motor dysfunction in PD. Mean tap velocity appeared to be the best parameter to differentiate patients with PD from controls. Patients with anosmia also showed detectable differences in motor performance compared with controls which may suggest that some were in the prodromal phase of PD.

Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges

Monitoring of motor symptom fluctuations in Parkinson's disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation's occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56-96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.

Supervised classification of bradykinesia in Parkinson's disease from smartphone videos

BACKGROUND

Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best.

AIM

We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia.

METHODS

We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0-1) or mild/moderate/severe bradykinesia (UPDRS = 2-4), and presence or absence of Parkinson's diagnosis.

RESULTS

A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67.

CONCLUSION

The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

Parkinson's disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective

BACKGROUND

Parkinson's disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson's disease due to the limitations in current clinic assessment methods such as Unified Parkinson's Disease Rating Scale (UPDRS) and the Hoehn and Yahr (HY) scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users' perspectives in terms of device design and implementation. This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, and requirements of wearable devices.

METHODS

This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using an inductive thematic approach.

RESULTS

It was noted that the well-known assessment process such as Unified Parkinson's Disease Rating Scale (UPDRS) was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients' memories. Participants suggested that objective assessment methods are needed to increase the chance of effective treatment. The participants' perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor.

CONCLUSION

The study concluded that current assessment methods are limited. Patients' and healthcare professionals' involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.

A Multi-Sensor Wearable System for the Quantitative Assessment of Parkinson's Disease

The quantitative characterization of movement disorders and their related neurophysiological signals is important for the management of Parkinson’s disease (PD). The aim of this study is to develop a novel wearable system enabling the simultaneous measurement of both motion and other neurophysiological signals in PD patients. We designed a wearable system that consists of five motion sensors and three electrophysiology sensors to measure the motion signals of the body, electroencephalogram, electrocardiogram, and electromyography, respectively. The data captured by the sensors are transferred wirelessly in real time, and the outcomes are analyzed and uploaded to the cloud-based server automatically. We completed pilot studies to (1) test its validity by comparing outcomes to the commercialized systems, and (2) evaluate the deep brain stimulation (DBS) treatment effects in seven PD patients. Our results showed: (1) the motion and neurophysiological signals measured by this wearable system were strongly correlated with those measured by the commercialized systems (r > 0.94, p < 0.001); and (2) by completing the clinical supination and pronation frequency test, the frequency of motion as measured by this system increased when DBS was turned on. The results demonstrated that this multi-sensor wearable system can be utilized to quantitatively characterize and monitor motion and neurophysiological PD.

Deep brain stimulation: a review of the open neural engineering challenges

OBJECTIVE

Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on 'sensing mode' to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we will review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy.

APPROACH

We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS.

MAIN RESULTS

We identify then the main operative challenges toward optimal DBS such as (i) accurate target localization, (ii) increased spatial resolution of stimulation, (iii) development of in silico tests for DBS, (iv) identification of specific motor symptoms biomarkers, in particular (v) assessing how LFP oscillations relate to behavioral disfunctions, and (vi) clarify how stimulation affects the cortico-basal-ganglia-thalamic network to (vii) design optimal stimulation patterns.

SIGNIFICANCE

This roadmap will lead neural engineers novel to the field toward the most relevant open issues of DBS, while the in-depth readers might find a careful comparison of advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.

Parkinson's Disease: Available Clinical and Promising Omics Tests for Diagnostics, Disease Risk Assessment, and Pharmacotherapy Personalization

Parkinson's disease is the second most frequent neurodegenerative disease, representing a significant medical and socio-economic problem. Modern medicine still has no answer to the question of why Parkinson's disease develops and whether it is possible to develop an effective system of prevention. Therefore, active work is currently underway to find ways to assess the risks of the disease, as well as a means to extend the life of patients and improve its quality. Modern studies aim to create a method of assessing the risk of occurrence of Parkinson's disease (PD), to search for the specific ways of correction of biochemical disorders occurring in the prodromal stage of Parkinson's disease, and to personalize approaches to antiparkinsonian pharmacotherapy. In this review, we summarized all available clinically approved tests and techniques for PD diagnostics. Then, we reviewed major improvements and recent advancements in genomics, transcriptomics, and proteomics studies and application of metabolomics in PD research, and discussed the major metabolomics findings for diagnostics and therapy of the disease.

Evolving concepts on bradykinesia

Bradykinesia is one of the cardinal motor symptoms of Parkinson's disease and other parkinsonisms. The various clinical aspects related to bradykinesia and the pathophysiological mechanisms underlying bradykinesia are, however, still unclear. In this article, we review clinical and experimental studies on bradykinesia performed in patients with Parkinson's disease and atypical parkinsonism. We also review studies on animal experiments dealing with pathophysiological aspects of the parkinsonian state. In Parkinson's disease, bradykinesia is characterized by slowness, the reduced amplitude of movement, and sequence effect. These features are also present in atypical parkinsonisms, but the sequence effect is not common. Levodopa therapy improves bradykinesia, but treatment variably affects the bradykinesia features and does not significantly modify the sequence effect. Findings from animal and patients demonstrate the role of the basal ganglia and other interconnected structures, such as the primary motor cortex and cerebellum, as well as the contribution of abnormal sensorimotor processing. Bradykinesia should be interpreted as arising from network dysfunction. A better understanding of bradykinesia pathophysiology will serve as the new starting point for clinical and experimental purposes.

Upper Limb Interactive Weightless Technology-Aided Intervention and Assessment Picks Out Motor Skills Improvement in Parkinson's Disease: A Pilot Study

Background: In Parkinson's disease, reaching movements are conditioned by motor planning and execution deficiency. Recently, rehabilitation, aided by high technological devices, was employed for Parkinson's disease.

Objective: We aimed to (1) investigate the changes in the upper limb motor performances in a sample of a patient with Parkinson's disease after a weightless training, with a passive exoskeleton, in an augmented-feedback environment; (2) highlight differences by motor parameters (performance, speed, and movement accuracy) and by type of movement (simple or complex); and (3) evaluate movement improvements by UPDRS II–III.

Methods: Observational pilot study. Twenty right-handed patients with Parkinson's disease, Hohen and Yahr 2, Mini Mental State Examination ≥24 were evaluated. All patients underwent 5 day/week sessions for 4 weeks, 30 min for each arm; the training was performed with 12 exercises (single and multi-joints, horizontal and vertical movements). All the patients were assessed by UPDRS II–III and the evaluation tests provided by the device's software: a simple movement, the vertical capture, and a complex movement, the horizontal capture. For each test, we analyzed reached target percentage, movement execution time, and accuracy.

Results: After training, a significant improvement of accuracy and speed for simple movement on the dominant arm, of reached targets and speed for complex movement on both sides were shown. UPDRS II and III improved significantly after training.

Conclusions: In our study, a motor training aided by a high technological device improves motor parameters and highlights differences between the type of movement (simple or complex) and movement parameters (speed and accuracy) in a sample of patients with Parkinson's disease.

Parkinson's disease and wearable devices, new perspectives for a public health issue: an integrative literature review

Parkinson's disease is the second most common neurodegenerative disease, with an estimated prevalence of 41/100,000 individuals affected aged between 40 and 49 years old and 1,900/100,000 aged 80 and over. Based on the essentiality of ascertaining which wearable devices have clinical literary evidence and with the purpose of analyzing the information revealed by such technologies, we conducted this scientific article of integrative review. It is an integrative review, whose main objective is to carry out a summary of the state of the art of wearable devices used in patients with Parkinson's disease. After the review, we retrieved 8 papers. Of the selected articles, only 3 were not systematic reviews; one was a series of cases and two prospective longitudinal studies. These technologies have a very rich field of application; however, research is still necessary to make such evaluations reliable and crucial to the well-being of these patients.

Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease

Goal: In this paper we investigated the use of smartphone sensors and Artificial Intelligence techniques for the automatic quantification of the MDS-UPDRS-Part III Leg Agility (LA) task, representative of lower limb bradykinesia. Methods: We collected inertial data from 93 PD subjects. Four expert neurologists provided clinical evaluations. We employed a novel Artificial Neural Network approach in order to get a continuous output, going beyond the MDS-UPDRS score discretization. Results: We found a Pearson correlation of 0.92 between algorithm output and average clinical score, compared to an inter-rater agreement index of 0.88. Furthermore, the classification error was less than 0.5 scale point in about 80% cases. Conclusions: We proposed an objective and reliable tool for the automatic quantification of the MDS-UPDRS Leg Agility task. In perspective, this tool is part of a larger monitoring program to be carried out during activities of daily living, and managed by the patients themselves.

Quantitative Assessment of Finger Movement Profile in a Visual-Motor Task Based on a Tablet Computer: The Application in Parkinson's Disease

BACKGROUND/OBJECTIVE

Easily applicable, quantitative assessment of movement is widely needed in various clinical settings, especially in the evaluation of Parkinson's disease (PD).

METHODS

We developed a highly repeatable tablet computer-based finger movement assessment system (FMAS) to record finger movement profile in a visual-motor task both in PD (n = 217) and healthy participants (n = 221).

RESULTS

We found age-related declines in finger movement performance among the healthy participants but not in PD patients with the FMAS. Significant differences in movement time (MT) and latency/MT ratio but not in latency were observed in PD patients as compared with healthy subjects (P < 0.000). Meanwhile, we identified the latency/MT ratio as the optimal parameter to differentiate PD from age-matched healthy subjects in an age-independent manner (cut-off 1.08 with corresponding AUC = 0.861). In addition, a significant correlation was found between finger movement parameters and the Hoehn and Yahr scale (H-Y scale), UPDRS III score and the duration of the disease in PD patients (P < 0.01).

CONCLUSION

It was suggested that the tablet computer-based evaluation of finger movement provided an easily applicable quantitative method to assess the conditions of PD patients.

The BRadykinesia Akinesia INcoordination (BRAIN) Tap Test: Capturing the Sequence Effect

BACKGROUND

The BRadykinesia Akinesia INcoordination (BRAIN) tap test is an online keyboard tapping task that has been previously validated to assess upper limb motor function in Parkinson's disease (PD).

OBJECTIVES

To develop a new parameter that detects a sequence effect and to reliably distinguish between PD patients on and off medication. In addition, we sought to validate a mobile version of the test for use on smartphones and tablet devices.

METHODS

The BRAIN test scores in 61 patients with PD and 93 healthy controls were compared. A range of established parameters captured number and accuracy of alternate taps. The new velocity score recorded the intertap speed. Decrement in the velocity score was used as a marker for the sequence effect. In the validation phase, 19 PD patients and 19 controls were tested using different hardware including mobile devices.

RESULTS

Quantified slopes from the velocity score demonstrated bradykinesia (sequence effect) in PD patients (slope cut-off -0.002) with 58% sensitivity and 81% specificity (discovery phase of the study) and 65% sensitivity and 88% specificity (validation phase). All BRAIN test parameters differentiated between on and off medication states in PD. Differentiation between PD patients and controls was possible on all hardware versions of the test.

CONCLUSION

The BRAIN tap test is a simple, user-friendly, and free-to-use tool for the assessment of upper limb motor dysfunction in PD, which now includes a measure of bradykinesia.

Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer

OBJECTIVE

The recent advances in technology are opening a new opportunity to remotely evaluate motor features in people with Parkinson's disease (PD). We hypothesized that typing on an electronic device, a habitual behavior facilitated by the nigrostriatal dopaminergic pathway, could allow for objectively and nonobtrusively monitoring parkinsonian features and response to medication in an at-home setting.

METHODS

We enrolled 31 participants recently diagnosed with PD who were due to start dopaminergic treatment and 30 age-matched controls. We remotely monitored their typing pattern during a 6-month (24 weeks) follow-up period before and while dopaminergic medications were being titrated. The typing data were used to develop a novel algorithm based on recursive neural networks and detect participants' responses to medication. The latter were defined by the Unified Parkinson's Disease Rating Scale-III (UPDRS-III) minimal clinically important difference. Furthermore, we tested the accuracy of the algorithm to predict the final response to medication as early as 21 weeks prior to the final 6-month clinical outcome.

RESULTS

The score on the novel algorithm based on recursive neural networks had an overall moderate kappa agreement and fair area under the receiver operating characteristic (ROC) curve with the time-coincident UPDRS-III minimal clinically important difference. The participants classified as responders at the final visit (based on the UPDRS-III minimal clinically important difference) had higher scores on the novel algorithm based on recursive neural networks when compared with the participants with stable UPDRS-III, from the third week of the study onward.

CONCLUSIONS

This preliminary study suggests that remotely gathered unsupervised typing data allows for the accurate detection and prediction of drug response in PD. © 2019 International Parkinson and Movement Disorder Society.

Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson's Disease

A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson’s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.

Objective evaluation of bradykinesia in Parkinson's disease using an inexpensive marker-less motion tracking system

OBJECTIVE

Quantification of bradykinesia (slowness of movement) is crucial for the treatment and monitoring of Parkinson's disease. Subjective observational techniques are the de-facto 'gold standard', but such clinical rating scales suffer from poor sensitivity and inter-rater variability. Although various technologies have been developed for assessing bradykinesia in recent years, most still require considerable expertise and effort to operate. Here we present a novel method to utilize an inexpensive off-the-shelf hand-tracker (Leap Motion) to quantify bradykinesia.

APPROACH

Eight participants with Parkinson's disease receiving benefit from deep brain stimulation were recruited for the study. Participants were assessed 'on' and 'off' stimulation, with the 'on' condition repeated to evaluate reliability. Participants performed wrist pronation/supination, hand open/close, and finger-tapping tasks during each condition. Tasks were simultaneously captured by our software and rated by three clinicians. A linear regression model was developed to predict clinical scores and its performance was assessed with leave-one-subject-out cross validation.

MAIN RESULTS

Aggregate bradykinesia scores predicted by our method were in strong agreement (R  =  0.86) with clinical scores. The model was able to differentiate therapeutic states and comparison between the test-retest conditions yielded no significant difference (p   =  0.50).

SIGNIFICANCE

These findings demonstrate that our method can objectively quantify bradykinesia in agreement with clinical observation and provide reliable measurements over time. The hardware is readily accessible, requiring only a modest computer and our software to perform assessments, thus making it suitable for both clinic- and home-based symptom tracking.

An update on adaptive deep brain stimulation in Parkinson's disease

Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Automated Assessment of Movement Impairment in Huntington's Disease

Quantitative assessment of movement impairment in Huntington's disease (HD) is essential to monitoring of disease progression. This paper aimed to develop and validate a novel low cost, objective automated system for the evaluation of upper limb movement impairment in HD in order to eliminate the inconsistency of the assessor and offer a more sensitive, continuous assessment scale. Patients with genetically confirmed HD and healthy controls were recruited to this observational study. Demographic data, including age (years), gender, and unified HD rating scale total motor score (UHDRS-TMS), were recorded. For the purposes of this paper, a modified upper limb motor impairment score (mULMS) was generated from the UHDRS-TMS. All participants completed a brief, standardized clinical assessment of upper limb dexterity while wearing a tri-axial accelerometer on each wrist and on the sternum. The captured acceleration data were used to develop an automatic classification system for discriminating between healthy and HD participants and to automatically generate a continuous movement impairment score (MIS) that reflected the degree of the movement impairment. Data from 48 healthy and 44 HD participants was used to validate the developed system, which achieved 98.78% accuracy in discriminating between healthy and HD participants. The Pearson correlation coefficient between the automatic MIS and the clinician rated mULMS was 0.77 with a p-value < 0.01. The approach presented in this paper demonstrates the possibility of an automated objective, consistent, and sensitive assessment of the HD movement impairment.

App-Based Bradykinesia Tasks for Clinic and Home Assessment in Parkinson's Disease: Reliability and Responsiveness

BACKGROUND

Clinical rating of bradykinesia in Parkinson disease (PD) is challenging as it must combine several movement features into a single score. Additionally, in-clinic assessment cannot capture fluctuations throughout the day.

OBJECTIVE

To evaluate the reliability and responsiveness of a motion sensor-based tablet app for objective bradykinesia assessment in clinic and at home as compared to clinical ratings.

METHODS

Thirty-two PD patients treated with subthalamic deep brain stimulation (DBS) were outfitted with a motion sensor on the index finger of the more affected hand to perform two repetitions of finger-tapping, hand opening-closing, and arm pronation-supination tasks with DBS on and 10, 20, and 30 minutes after turning DBS off. Tasks were videotaped for blinded clinician rating using the Modified Bradykinesia Rating Scale (MBRS). Participants were then sent home with an app-based system to perform two repetitions of the same tasks six times per day spaced two hours apart, three days per week, for two weeks. Intraclass correlation (ICC) and minimal detectable change (MDC) were calculated.

RESULTS

As the effects of DBS wore off, motion sensors detected worsening of amplitude sooner than did clinician-rated MBRS for all three tasks. ICCs were significantly higher and MDCs were significantly lower for motion sensors in the clinic and at home than for clinician ratings (p < 0.01).

CONCLUSIONS

The tablet-based app demonstrated higher reliability and responsiveness in capturing bradykinesia-related tasks in the clinic and at home than did clinician ratings. This tool may enhance the assessment of novel therapies.

Tablet-Based Application for Objective Measurement of Motor Fluctuations in Parkinson Disease

BACKGROUND

The motor subscale of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) has limited applicability for the assessment of motor fluctuations in the home setting.

METHODS

To assess whether a self-administered, tablet-based application can reliably quantify differences in motor performance using two-target finger tapping and forearm pronation-supination tasks in the ON (maximal dopaminergic medication efficacy) and OFF (reemergence of parkinsonian deficits) medication states, we recruited 11 Parkinson disease (PD) patients (age, 60.6 ± 9.0 years; disease duration, 12.8 ± 4.1 years) and 11 healthy age-matched controls (age, 62.5 ± 10.5 years). The total number of taps, tap interval, tap duration, and tap accuracy were algorithmically calculated by the application, using the more affected side in patients and the dominant hand in healthy controls.

RESULTS

Compared to the OFF state, PD patients showed a higher number of taps (84.2 ± 20.3 vs. 54.9 ± 26.9 taps; p = 0.0036) and a shorter tap interval (375.3 ± 97.2 vs. 708.2 ± 412.8 ms; p = 0.0146) but poorer tap accuracy (2,008.4 ± 995.7 vs. 1,111.8 ± 901.3 pixels; p = 0.0055) for the two-target task in the ON state, unaffected by the magnitude of coexistent dyskinesia. Overall, test-retest reliability was high (r >0.75) and the discriminatory ability between OFF and ON states was good (0.60 ≤ AUC ≤ 0.82). The correlations between tapping data and MDS-UPDRS-III scores were only moderate (-0.55 to 0.55).

CONCLUSIONS

A self-administered, tablet-based application can reliably distinguish between OFF and ON states in fluctuating PD patients and may be sensitive to additional motor phenomena, such as accuracy, not captured by the MDS-UPDRS-III.