Finger tapping analysis in patients with Parkinson's disease and atypical parkinsonism

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 use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review

Developing drugs for rare diseases is challenging, and the precision and objectivity of outcome measures is critical to this process. In recent years, a number of technologies have increasingly been used for remote monitoring of patient health. We report a systematic literature review that aims to summarize the current state of progress with regard to the use of digital outcome measures for real-life motor function assessment of patients with rare neurological diseases. Our search of published literature identified 3826 records, of which 139 were included across 27 different diseases. This review shows that use of digital outcome measures for motor function outside a clinical setting is feasible and employed in a broad range of diseases, although we found few outcome measures that have been robustly validated and adopted as endpoints in clinical trials. Future research should focus on validation of devices, variables, and algorithms to allow for regulatory qualification and widespread adoption.

The Sequence Effect Worsens Over Time in Parkinson's Disease and Responds to Open and Closed-Loop Subthalamic Nucleus Deep Brain Stimulation

BACKGROUND

The sequence effect is the progressive deterioration in speech, limb movement, and gait that leads to an inability to communicate, manipulate objects, or walk without freezing of gait. Many studies have demonstrated a lack of improvement of the sequence effect from dopaminergic medication, however few studies have studied the metric over time or investigated the effect of open-loop deep brain stimulation in people with Parkinson's disease (PD).

OBJECTIVE

To investigate whether the sequence effect worsens over time and/or is improved on clinical (open-loop) deep brain stimulation (DBS).

METHODS

Twenty-one people with PD with bilateral subthalamic nucleus (STN) DBS performed thirty seconds of instrumented repetitive wrist flexion extension and the MDS-UPDRS III off therapy, prior to activation of DBS and every six months for up to three years. A sub-cohort of ten people performed the task during randomized presentations of different intensities of STN DBS.

RESULTS

The sequence effect was highly correlated with the overall MDS-UPDRS III score and the bradykinesia sub-score and worsened over three years. Increasing intensities of STN open-loop DBS improved the sequence effect and one subject demonstrated improvement on both open-loop and closed-loop DBS.

CONCLUSION

Sequence effect in limb bradykinesia worsened over time off therapy due to disease progression but improved on open-loop DBS. These results demonstrate that DBS is a useful treatment of the debilitating effects of the sequence effect in limb bradykinesia and upon further investigation closed-loop DBS may offer added improvement.

The effect of standing posture on amplitude and variability of postural tremor in Parkinson’s disease

INTRODUCTION

This study examined whether altering body position (i.e., sitting or standing) affected the dynamics of physiological tremor for healthy older adults and persons with Parkinson's disease (PD). It was also of interest to determine how consistent the tremor was for both groups as determined by examining changes in within-subject variability of tremor amplitude, regularity and frequency.

METHODS

Ten Parkinsonian participants (65.1±3.2 yrs.) and twelve elderly persons (71.2±2.6 yrs.) participated in this study. Tremor was collected from the index finger and hand segments using lightweight accelerometers during the performance of a bilateral pointing task. Persons performed the pointing task in a standing or sitting position.

RESULTS

As expected, the tremor for the PD persons was greater in magnitude (mean RMS, peak power), more regular (lower SampEn), and more inconsistent from trial-to-trial (increased intra-individual variability, IIV) than the tremor recorded for the elderly. Further, when assessed during standing, the magnitude of the tremor for all individuals (elderly and PD) was greater, more variable, and less complex compared to the tremor when assessed during the sitting posture. The only measure which did not change within each group was the frequency of the major tremor peak which remained consistent, showing no significant change between limbs or as a function of the posture adopted.

CONCLUSION

The findings revealed that tremor increased in amplitude and decreased in regularity for all individuals was assessed when standing compared to sitting. It is likely that these increases were task-related, reflecting the increased physical demands of performing the task when standing rather than being driven by specific age- or disease-related changes in the mechanisms underlying tremorgenesis. Further, the tremor for the PD individuals tended to be more variable from trial-by-trial in terms of both amplitude and regularity as compared to the elderly persons. Interestingly, the only tremor metric which showed no change within each group was the frequency of the major tremor peak which was consistent within both groups irrespective of the posture adopted.

Bradykinesia in Neurodegenerative Disorders: A Blinded Video Analysis of Pathology-Proven Cases

BACKGROUND

Bradykinesia is a cardinal feature in parkinsonisms. No study has assessed the differential features of bradykinesia in patients with pathology-proven synucleinopathies and tauopathies.

OBJECTIVE

We examined whether bradykinesia features (speed, amplitude, rhythm, and sequence effect) may differ between pathology-proven synucleinopathies and tauopathies.

METHODS

Forty-two cases who underwent autopsy were included and divided into synucleinopathies (Parkinson's disease and dementia with Lewy bodies) and tauopathies (progressive supranuclear palsy). Two raters blinded to the diagnosis retrospectively scored the Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III and Modified Bradykinesia Rating Scale on standardized videotaped neurological examinations. Bradykinesia scores were compared using the Mann-Whitney test and logistic regression models to adjust for disease duration.

RESULTS

Demographic and clinical parameters were similar between synucleinopathies and tauopathies. There were no differences between speed, amplitude, rhythm, and sequence effect in synucleinopathies and tauopathies in unadjusted comparisons and adjusted models (all P > 0.05).

CONCLUSIONS

Clinical bradykinesia features do not distinguish the underlying neuropathology in neurodegenerative parkinsonisms. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms

Background

Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms.

Methods

A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC).

Results

The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups.

Conclusions

The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research.

Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review

Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

Use of Functional Data to Model the Trajectory of an IMU and Classify Levels of Motor Impairment for Stroke Patients

Motor impairment evaluations are key rehabilitation-related assessments for patients with stroke. Currently, such evaluations are subjective; they are based on physicians' judgements regarding the actions performed by patients. This leads to inconsistent clinical results. Many inertial sensing elements for motion detection have been designed. However, to more easily and rapidly evaluate motor impairment, we require a system that can collect data effectively to predict the degree of motor impairment. Lin et al. used data gloves equipped with an inertial measurement unit (IMU) to collect movement trajectories for motor impairment evaluations in patients with stroke. The present study used functional data analysis to model data trajectories to reduce the influence of noise from IMU data and proposed using coefficients of function as features for classifying motor impairment. To verify the appropriateness of feature construction, five classification methods were used to evaluate the extracted features in terms of the overall and sensor-specific ability to classify levels of motor impairment. The results indicated that the features derived from cubic smoothing splines could effectively reflect key data characteristics, and a support vector machine yielded relatively high overall and sensor-specific accuracy for distinguishing between levels of motion impairment in patients with stroke. Future data glove systems can contain cubic smoothing splines to extract hand function features and then classify motion impairment for appropriate rehabilitation programs to be prescribed.

Bradykinesia in motoneuron diseases

OBJECTIVE

Only few studies investigated voluntary movement abnormalities in patients with motoneuron diseases (MNDs) or their neurophysiological correlates. We aimed to kinematically assess finger tapping abnormalities in patients with amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS), as compared to healthy controls (HCs), and their relationship with motoneuron involvement.

METHODS

Fourteen ALS and 5 PLS patients were enrolled. Finger tapping was assessed by a motion analysis system. Patients underwent a central motor conduction time assessment, a motor nerve conduction study, and needle electromyography. Data were compared to those of 79 HCs using non-parametric tests. Possible relationships between clinical, kinematic, and neurophysiological data were assessed in patients.

RESULTS

As a major finding, ALS and PLS patients performed finger tapping slower than HCs. In both conditions, movement slowness correlated with muscle strength. In ALS, movement slowness also correlated with the amplitude of the compound muscle action potential recorded from the muscles involved in the task and with denervation activity. No correlations were found between slowness, measures of upper motoneuron involvement, and other clinical and neurophysiological data.

CONCLUSIONS

This study provides novel information on voluntary movement abnormalities in MNDs.

SIGNIFICANCE

The results highlight the pathophysiological role of motoneurons in generating movement slowness.

Prediction of motor Unified Parkinson's Disease Rating Scale scores in patients with Parkinson's disease using surface electromyography

OBJECTIVE

Parkinson's disease (PD) is a chronic neurodegenerative disorder with increasing prevalence in the elderly. Especially patients with advanced PD often require complex medication regimens due to fluctuations, that is abrupt transitions from ON to OFF or vice versa. Current gold standard to quantify PD-patients' motor symptoms is the assessment of the Unified Parkinson's Disease Rating Scale (UPDRS), which, however, is cumbersome and may depend upon investigators. This work aimed at developing a mobile, objective and unobtrusive measurement of motor symptoms in PD.

METHODS

Data from 45 PD-patients was recorded using surface electromyography (sEMG) electrodes attached to a wristband. The motor paradigm consisted of a tapping task performed with and without dopaminergic medication. Our aim was to predict UPDRS scores from the sEMG characteristics with distinct regression models and machine learning techniques.

RESULTS

A random forest regression model outnumbered other regression models resulting in a correlation of 0.739 between true and predicted UPDRS values.

CONCLUSIONS

PD-patients' motor affection can be extrapolated from sEMG data during a simple tapping task. In the future, such records could help determine the need for medication changes in telemedicine applications.

SIGNIFICANCE

Our findings support the utility of wearables to detect Parkinson's symptoms and could help in developing tailored therapies in the future.

The relation between falls risk and movement variability in Parkinson's disease

Falls are a major health concern for older adults with Parkinson's disease (PD). This study was designed to examine differences in falls risk and its relation to changes in the average and variability (i.e. intra-individual variability) of reaction time (RT), finger tapping, standing balance and walking between healthy older adults and persons with PD. Thirty-nine adults with PD (70.0 ± 8.1 years) and 29 healthy older adults (66.8 ± 10.4 years) participated in this study. Falls risk (using the physiological profile assessment), gait, RT, balance and tapping responses were assessed for all persons. Results demonstrated that individuals with PD exhibited a greater risk of falling coupled with a general slowing of motor function covering declines in walking, RT and finger tapping. In addition, the movement responses of the PD group were more variable than the healthy older adults. Correlation results revealed group differences with regards to the neuromotor measures which were significantly correlated with falls risk. For the PD group, gait measures were highly correlated with their falls risk while, for the healthy older adults, falls risk was linked to balance measures even though PD persons had increased sway. Overall, persons with PD were at greater falls risk, moved slower and with increased variability compared to the healthy older adults. Further, while there are some similarities between the two groups in terms of those measures related to falls risk, there were also several differences which highlight that persons with PD can have different risk factors for falling compared to healthy adults of similar age.

Quantifying upper limb motor impairment in people with Parkinson's disease: a physiological profiling approach

BACKGROUND

Upper limb motor impairments, such as slowness of movement and difficulties executing sequential tasks, are common in people with Parkinson's disease (PD).

OBJECTIVE

To evaluate the validity of the upper limb Physiological Profile Assessment (PPA) as a standard clinical assessment battery in people with PD, by determining whether the tests, which encompass muscle strength, dexterity, arm stability, position sense, skin sensation and bimanual coordination can (a) distinguish people with PD from healthy controls, (b) detect differences in upper limb test domains between "off" and "on" anti-Parkinson medication states and (c) correlate with a validated measure of upper limb function.

METHODS

Thirty-four participants with PD and 68 healthy controls completed the upper limb PPA tests within a single session.

RESULTS

People with PD exhibited impaired performance across most test domains. Based on validity, reliability and feasibility, six tests (handgrip strength, finger-press reaction time, 9-hole peg test, bimanual pole test, arm stability, and shirt buttoning) were identified as key tests for the assessment of upper limb function in people with PD.

CONCLUSIONS

The upper limb PPA provides a valid, quick and simple means of quantifying specific upper limb impairments in people with PD. These findings indicate clinical assessments should prioritise tests of muscle strength, unilateral movement and dexterity, bimanual coordination, arm stability and functional tasks in people with PD as these domains are the most commonly and significantly impaired.

[Parkinson's disease and it's look-alike]

The diagnosis of Parkinson's disease (PD) requires the exclusion of other diseases using various methods. However, it is difficult to differentiate these diseases based only on clinical symptoms, and information regarding responses to drugs and several imaging examinations are often needed for a diagnosis. In recent years, various neurological signs and symptoms have been reported that are particularly useful in neurological examinations for differentiating PD, progressive supranuclear palsy, and multiple system atrophy. Currently, diagnosis using imaging techniques and artificial intelligence are being developed, but systematic neurological examinations will continue to be important in diagnosing these diseases.

SensRing, a novel wearable ring-shaped device for objective analysis of reachto-grasp movements

Reach-to-grasp actions have been recently studied to highlight how intentions influence action planning and shapes the movement kinematics. Reach-to-grasp (RG) kinematics can reveal important information on motor planning and control in several pathologies, including neurodegenerative diseases. Current methods are mainly based on optoelectronic analysis systems, which provide accurate movement tracking but are expensive, time-consuming, and limited to constrained research-oriented space. In this study, we proposed an innovative, non-invasive, and easy-to-use ringshaped wearable system, named SensRing, able to record inertial data during the movement. To ensure accurate and precise measures, which are mandatory for clinical practice, a preliminary technical validation of the SensRing with respect to the Vicon (i.e., gold standard for motion analysis) was performed on two finger tapping exercises. Preliminary results pointed out very low discrepancies in terms of absolute errors (AbsErr) between the values of repetitions (AbsErr≤0.8), frequency (AbsErr=0.04Hz) and amplitude (AbsErr≤2.7deg) measured by the two systems, as well as high correlation between the measures obtained with the inertial and optical system. Therefore, inertial data from the SensRing were used in a "reach-to-grasp and move" protocol to calculate the performance of a group of healthy young subjects during three RG and move sequences. Particularly, subjects were instructed to reach and grasp a bottle to drink (DRINK), to place it on the table (IND) or to pass it to another partner (SOC). Results showed that SensRing could identify that, in the RG phase, different intentions determine different kinematic parameters of grasping the same object. As concerns the phase of moving, if the movement is different (drink vs IND/SOC) it's easier to find differences between the tasks, but also when the action is the same but with different social intent (IND vs SOC) SensRing found a significant difference.

Wearable Sensors for Prodromal Motor Assessment of Parkinson's Disease using Supervised Learning

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. Idiopathic hyposmia (IH), a reduced olfactory sensitivity, is a preclinical marker for the pathology and affects >95% of PD patients. In this paper, SensHand V1 and SensFoot V2, two inertial wearable sensors for upper and lower limbs, were developed to acquire motion data in ten tasks of the MDS-UPDRS III. Fifteen healthy subjects of control, 15 IH people, and 15 PD patients were enrolled. Seventy-one parameters per side were computed by spatiotemporal and frequency data analysis, and the most significant were selected to distinguish among the different classes. Performances of supervised learning algorithms (i.e., Support Vector Machine (SVM), and Random Forest (RF)) were compared on two-group and three-group classification and considering upper and lower limbs separately or together as a full system. Excellent results were obtained for healthy vs. patients classification (accuracy 1.00 for RF, and 0.97 for SVM), and good results were achieved by including IH subjects (0.92 F-measure with RF) within a three-group classification. Overall, the best performances were obtained using the full system with an RF classifier. The system is, thus, suitable to support an objective PD diagnosis. Furthermore, combining motion analysis with a validated olfactory screening test, people at risk for PD can be appropriately analyzed, and subtle changes in motor performance that characterize the prodromal phase and the early PD onset can be identified.

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.

[Remote assessment of idiopathic Parkinson's disease : Developments in diagnostics, monitoring and treatment]

The idiopathic Parkinson's disease (iPD) is a progressive neurodegenerative disorder primarily resulting in impaired movement execution. In the course of the disease symptom fluctuation is common and makes adequate treatment difficult. In this overview the current approaches using modern and especially mobile technologies for diagnosis, monitoring and treatment of iPD are presented. Currently, there are no medical aids ready for point of care application; however, the development of these technologies has great potential for improving care for patients suffering from iPD.

An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors

Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson's disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.

Technologies Assessing Limb Bradykinesia in Parkinson's Disease

Background: The MDS-UPDRS (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale) is the most widely used scale for rating impairment in PD. Subscores measuring bradykinesia have low reliability that can be subject to rater variability. Novel technological tools can be used to overcome such issues.

Objective: To systematically explore and describe the available technologies for measuring limb bradykinesia in PD that were published between 2006 and 2016.

Methods: A systematic literature search using PubMed (MEDLINE), IEEE Xplore, Web of Science, Scopus and Engineering Village (Compendex and Inspec) databases was performed to identify relevant technologies published until 18 October 2016.

Results: 47 technologies assessing bradykinesia in PD were identified, 17 of which offered home and clinic-based assessment whilst 30 provided clinic-based assessment only. Of the eligible studies, 7 were validated in a PD patient population only, whilst 40 were tested in both PD and healthy control groups. 19 of the 47 technologies assessed bradykinesia only, whereas 28 assessed other parkinsonian features as well. 33 technologies have been described in additional PD-related studies, whereas 14 are not known to have been tested beyond the pilot phase.

Conclusion: Technology based tools offer advantages including objective motor assessment and home monitoring of symptoms, and can be used to assess response to intervention in clinical trials or routine care. This review provides an up-to-date repository and synthesis of the current literature regarding technology used for assessing limb bradykinesia in PD. The review also discusses the current trends with regards to technology and discusses future directions in development.

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology.

OBJECTIVES

This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring).

DATA SOURCES

The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016.

STUDY ELIGIBILITY CRITERIA

Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers.

RESULTS

Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

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.

Clinical Assessments in Parkinson's Disease: Scales and Monitoring

Measurement of disease state is essential in both clinical practice and research in order to assess the severity and progression of a patient's disease status, effect of treatment, and alterations in other relevant factors. Parkinson's disease (PD) is a complex disorder expressed through many motor and nonmotor manifestations, which cause disabilities that can vary both gradually over time or come on suddenly. In addition, there is a wide interpatient variability making the appraisal of the many facets of this disease difficult. Two kinds of measure are used for the evaluation of PD. The first is subjective, inferential, based on rater-based interview and examination or patient self-assessment, and consist of rating scales and questionnaires. These evaluations provide estimations of conceptual, nonobservable factors (e.g., symptoms), usually scored on an ordinal scale. The second type of measure is objective, factual, based on technology-based devices capturing physical characteristics of the pathological phenomena (e.g., sensors to measure the frequency and amplitude of tremor). These instrumental evaluations furnish appraisals with real numbers on an interval scale for which a unit exists. In both categories of measures, a broad variety of tools exist. This chapter aims to present an up-to-date summary of the most relevant characteristics of the most widely used scales, questionnaires, and technological resources currently applied to the assessment of PD. The review concludes that, in our opinion: (1) no assessment methods can substitute the clinical judgment and (2) subjective and objective measures in PD complement each other, each method having strengths and weaknesses.

Quantification of Finger-Tapping Angle Based on Wearable Sensors

We propose a novel simple method for quantitative and qualitative finger-tapping assessment based on miniature inertial sensors (3D gyroscopes) placed on the thumb and index-finger. We propose a simplified description of the finger tapping by using a single angle, describing rotation around a dominant axis. The method was verified on twelve subjects, who performed various tapping tasks, mimicking impaired patterns. The obtained tapping angles were compared with results of a motion capture camera system, demonstrating excellent accuracy. The root-mean-square (RMS) error between the two sets of data is, on average, below 4°, and the intraclass correlation coefficient is, on average, greater than 0.972. Data obtained by the proposed method may be used together with scores from clinical tests to enable a better diagnostic. Along with hardware simplicity, this makes the proposed method a promising candidate for use in clinical practice. Furthermore, our definition of the tapping angle can be applied to all tapping assessment systems.