Quantifying Parkinson's disease finger-tapping severity by extracting and synthesizing finger motion properties

Deep learning of Parkinson's movement from video, without human-defined measures

BACKGROUND

The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures.

OBJECTIVES

(1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model.

METHODS

152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips.

RESULTS

For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group.

CONCLUSIONS

A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features.

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.

A Three-Dimensional Finger-Tapping Framework for Recognition of Patients With Mild Parkinson's Disease

The finger tapping test is a widely-used and important examination in the Movement Disorder Society Clinical Diagnosis for Parkinson's Disease. However, finger tapping motion could be affected by age, medication, and other conditions. As a result, Parkinson's disease patients with mild sign and healthy people could be rated as similar scores on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale, making it difficult for community doctors to perform diagnosis. We therefore propose a three-dimensional finger tapping framework to recognize mild PD patients. Specifically, we first derive the three-dimensional finger-tapping motion using a self-designed three-dimensional finger-tapping measurement system. We then propose a three-dimensional finger-tapping segmentation algorithm to segment three-dimensional finger tapping motion. We next extract three-dimensional pattern features of motor coordination, imbalance impairment, and entropy. We finally adopted the support vector machine as the classifier to recognize PD patients. We evaluated the proposed framework on 49 PD patients and 29 healthy controls and reached an accuracy of 94.9% for the right hand and 89.4% for the left hand. Moreover, the proposed framework reached an accuracy of 95.0% for the right hand and 97.8% for the left hand on 17 mild PD patients and 28 healthy controls who were both rated as 0 or 1 on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale. The results demonstrated that the proposed framework was less sensitive to traditional features and performed well in recognizing mild PD patients by involving three-dimensional patter features.

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

BACKGROUND

Bradykinesia is considered the fundamental motor feature of Parkinson's disease (PD). It is central to diagnosis, monitoring, and research outcomes. However, as a clinical sign determined purely by visual judgement, the reliability of humans to detect and measure bradykinesia remains unclear.

OBJECTIVE

To establish interrater reliability for expert neurologists assessing bradykinesia during the finger tapping test, without cues from additional examination or history.

METHODS

21 movement disorder neurologists rated finger tapping bradykinesia, by Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Modified Bradykinesia Rating Scale (MBRS), in 133 videos of hands: 73 from 39 people with idiopathic PD, 60 from 30 healthy controls. Each neurologist rated 30 randomly-selected videos. 19 neurologists were also asked to judge whether the hand was PD or control. We calculated intraclass correlation coefficients (ICC) for absolute agreement and consistency of MDS-UPDRS ratings, using standard linear and cumulative linked mixed models.

RESULTS

There was only moderate agreement for finger tapping MDS-UPDRS between neurologists, ICC 0.53 (standard linear model) and 0.65 (cumulative linked mixed model). Among control videos, 53% were rated >  0 by MDS-UPDRS, and 24% were rated as bradykinesia by MBRS subscore combination. Neurologists correctly identified PD/control status in 70% of videos, without strictly following bradykinesia presence/absence.

CONCLUSION

Even experts show considerable disagreement about the level of bradykinesia on finger tapping, and frequently see bradykinesia in the hands of those without neurological disease. Bradykinesia is to some extent a phenomenon in the eye of the clinician rather than simply the hand of the person with PD.

Influence of vibrotactile random noise on the smoothness of the grasp movement in patients with chemotherapy-induced peripheral neuropathy

Patients with chemotherapy-induced peripheral neuropathy (CIPN) often suffer from sensorimotor dysfunction of the distal portion of the extremities (e.g., loss of somatosensory sensation, numbness/tingling, difficulty typing on a keyboard, or difficulty buttoning/unbuttoning a shirt). The present study aimed to reveal the effects of subthreshold vibrotactile random noise stimulation on sensorimotor dysfunction in CIPN patients without exacerbating symptoms. Twenty-five patients with CIPN and 28 age-matched healthy adults participated in this study. To reveal the effects of subthreshold vibrotactile random noise stimulation on sensorimotor function, participants were asked to perform a tactile detection task and a grasp movement task during random noise stimulation delivered to the volar and dorsal wrist. We set three intensity conditions of the vibrotactile random noise: 0, 60, and 120% of the sensory threshold (Noise 0%, Noise 60%, and Noise 120% conditions). In the tactile detection task, a Semmes-Weinstein monofilament was applied to the volar surface of the tip of the index finger using standard testing measures. In the grasp movement task, the distance between the thumb and index finger was recorded while the participant attempted to grasp a target object, and the smoothness of the grasp movement was quantified by calculating normalized jerk in each experimental condition. The experimental data were compared using two-way repeated-measures analyses of variance with two factors: experimental condition (Noise 0, 60, 120%) × group (Healthy controls, CIPN patients). The tactile detection threshold and the smoothness of the grasp movement were only improved in the Noise 60% condition without exacerbating numbness/tingling in CIPN patients and healthy controls. The current study suggested that the development of treatment devices using stochastic resonance can improve sensorimotor function for CIPN patients.

SARN: Shifted Attention Regression Network for 3D Hand Pose Estimation

Hand pose estimation (HPE) plays an important role during the functional assessment of the hand and in potential rehabilitation. It is a challenge to predict the pose of the hand conveniently and accurately during functional tasks, and this limits the application of HPE. In this paper, we propose a novel architecture of a shifted attention regression network (SARN) to perform HPE. Given a depth image, SARN first predicts the spatial relationships between points in the depth image and a group of hand keypoints that determine the pose of the hand. Then, SARN uses these spatial relationships to infer the 3D position of each hand keypoint. To verify the effectiveness of the proposed method, we conducted experiments on three open-source datasets of 3D hand poses: NYU, ICVL, and MSRA. The proposed method achieved state-of-the-art performance with 7.32 mm, 5.91 mm, and 7.17 mm of mean error at the hand keypoints, i.e., mean Euclidean distance between the predicted and ground-truth hand keypoint positions. Additionally, to test the feasibility of SARN in hand movement recognition, a hand movement dataset of 26K depth images from 17 healthy subjects was constructed based on the finger tapping test, an important component of neurological exams administered to Parkinson's patients. Each image was annotated with the tips of the index finger and the thumb. For this dataset, the proposed method achieved a mean error of 2.99 mm at the hand keypoints and comparable performance on three task-specific metrics: the distance, velocity, and acceleration of the relative movement of the two fingertips. Results on the open-source datasets demonstrated the effectiveness of the proposed method, and results on our finger tapping dataset validated its potential for applications in functional task characterization.

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.

Measurement of upper limb function in ALS: a structure review of current methods and future directions

Measurement of upper limb function is critical for tracking clinical severity in amyotrophic lateral sclerosis (ALS). The Amyotrophic Lateral Sclerosis Rating Scale-revised (ALSFRS-r) is the primary outcome measure utilised in clinical trials and research in ALS. This scale is limited by floor and ceiling effects within subscales, such that clinically meaningful changes for subjects are often missed, impacting upon the evaluation of new drugs and treatments. Technology has the potential to provide sensitive, objective outcome measurement. This paper is a structured review of current methods and future trends in the measurement of upper limb function with a particular focus on ALS. Technologies that have the potential to radically change the upper limb measurement field and explore the limitations of current technological sensors and solutions in terms of costs and user suitability are discussed. The field is expanding but there remains an unmet need for simple, sensitive and clinically meaningful tests of upper limb function in ALS along with identifying consensus on the direction technology must take to meet this need.

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.

Boys with attention-deficit/hyperactivity disorder perform wider and fewer finger tapping than typically developing boys - Peer comparisons and the effects of methylphenidate from an exploratory perspective

AIM

This study aimed to investigate the differences in fine motor and coordination skills between boys with attention-deficit/hyperactivity disorder (ADHD) and typically developing (TD) boys and the effect of methylphenidate (MPH) in boys with ADHD.

METHODS

Fourteen boys aged 7-12 years who were diagnosed with ADHD and previously treated with MPH were instructed to tap their thumbs and index fingers together repetitively for 10 s after attaching magnetic sensors. The participants executed "in-phase" and "anti-phase" tapping. A two-way analysis of variance for comparing boys with ADHD and TD boys and the paired t-test to investigate the effect of MPH between sessions with and without MPH were performed.

RESULTS

Boys with ADHD showed a significantly lower "number of taps" and a significantly higher "average of local maximum distance" than TD boys. "Energy balance" was significantly lower in ADHD boys than in TD boys. MPH caused a significant difference in the "standard deviation (SD) of phase difference" in "anti-phase tapping."

CONCLUSION

Our studies indicated that finger-tapping movements in boys with ADHD tended to be significantly wider and fewer than those in TD boys, and MPH may improve the phase difference of bimanual fine motor coordination skills in boys with ADHD who are above 1.0 SD. The results should be interpreted with caution because we conducted statistical tests for many outcomes and groups without considering the multiplicity factor from an exploratory perspective.

Parkinson's disease severity clustering based on tapping activity on mobile device

In this study, we investigated the relationship between finger tapping tasks on the smartphone and the MDS-UPDRS I–II and PDQ-8 using the mPower dataset. mPower is a mobile application-based study for monitoring key indicators of PD progression and diagnosis. Currently, it is one of the largest, open access, mobile Parkinson’s Disease studies. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS I–II, PDQ-8, memory, tapping, voice, and walking. Finger-tapping is one of the tasks that easy to perform and has been analyzed for the quantitative measurement of PD. Therefore, participants who performed both the tapping activity and MDS-UPDRS I–II rating scale were selected for our analysis. Note that the MDS-UPDRS mPower Survey only contains parts of the original scale and has not been clinimetrically tested for validity and reliability. We obtained a total of 1851 samples that contained the tapping activity and MDS-UPDRS I–II for the analysis. Nine features were selected to represent tapping activity. K-mean was applied as an unsupervised clustering algorithm in our study. For determining the number of clusters, the elbow method, Sihouette score, and Davies–Bouldin index, were employed as supporting evaluation metrics. Based on these metrics and expert opinion, we decide that three clusters were appropriate for our study. The statistical analysis found that the tapping features could separate participants into three severity groups. Each group has different characteristics and could represent different PD severity based on the MDS-UPDRS I–II and PDQ-8 scores. Currently, the severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependant on the skills and experiences of the trained movement disorder specialist who performs the procedure. We believe that any additional methods that could potentially assist with quantitative assessment of disease severity, without the need for a clinical visit would be beneficial to both the healthcare professionals and patients.

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.

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.

Three-Dimensional Pattern Features in Finger Tapping Test for Patients with Parkinson's disease

Finger tapping test is an important neuropsychological test to evaluate human motor function. Most recent researches simplified the finger tapping motion as a scissors-like motion, though the rotation axis of the thumb was different from that of the forefinger. In this paper, we proposed a three-dimensional (3-D) finger tapping measurement system to obtain 3-D pattern features in finger tapping test for patients with Parkinson's disease (PD). The proposed system collected the motion of the thumb and the forefinger by nine-degrees-freedom sensors and calculated 3-D motion of finger tapping by an orientation estimation method and a 3-D finger-tapping kinematic model. We further extracted 3-D pattern features, i.e. motor coordination and relative thumb motion, from 3-D Finger Tapping motion. Moreover, we used the proposed system to collect the finger-tapping motion of 43 PD patients and 30 healthy controls in horizontal tasks and vertical tasks. The results indicated that 3-D pattern features showed a better performance than one-dimensional features in the identification of mild PD patients.Clinical Relevance- These three-dimensional pattern features could be used to evaluate finger tapping motion in a novel way, which could be used to better identify mild Parkinson's disease patients. Furthermore, the results showed that a combination of horizontal tasks and vertical tasks might be a better way to identify mild Parkinson's disease patients.

Quantitative assessment of fine motor skills in children using magnetic sensors

AIM

We aimed to establish objective and quantitative data on fine motor development in typically developing children using magnetic sensors.

METHODS

The study included 110 Japanese elementary school children volunteers (57 boys, 53 girls). The participants were instructed to tap their thumbs and index fingers together repetitively for 10 s. After attaching coils to the participants' right and left thumbs and index fingers, participants executed "in-phase" and "anti-phase" tapping. We used two-way analysis of variance to analyze the influences of age and sex on fine motor development.

RESULTS

The "number of taps" significantly increased with age, while the "standard deviation (SD) of tapping interval" significantly decreased. More than half of the "acceleration" parameters significantly increased with age. Boys performed significantly faster than girls in some parameters of "velocity" and "acceleration," while girls had significantly lower "SD of local maximum velocity in opening motion" and "SD of local minimum velocity in closing motion."

DISCUSSION

We established both objective and quantitative reference data on fine motor development in typically developing Japanese children aged between 7 and 12 years using magnetic sensors. We revealed that this system can monitor real-time details of the parameters involved in the finger-tapping movement in children without complications. This device could be useful for obtaining objective and quantitative data on fine motor skills in the clinical assessment of developmental coordination disorder, assessments of educational intervention, or rehabilitation and discovery of new therapeutic agents.

Stretchable and Highly Sensitive Optical Strain Sensors for Human-Activity Monitoring and Healthcare

Flexible and stretchable strain sensors are essential to developing smart wearable devices for monitoring human activities. Such sensors have been extensively exploited with various conductive materials and structures, which, however, are normally in need of complex manufacturing processes and confronted with the challenge to achieve both large stretchability and high sensitivity. Here, we report a simple and low-cost optical strategy for the design of stretchable strain sensors which are capable of measuring large strains of 100% with a low detection limit (±0.09%), a fast responsivity (<12 ms), and high reproducibility (over 6000 cycles). The optical strain sensor (OS²) is fabricated by assembling plasmonic gold nanoparticles (GNPs) in stretchable elastomer-based optical fibers, where a core/cladding structure with step-index configuration is adopted for light confinement. The stretchable, GNP-incorporated optical fiber shows strong localized surface plasmon resonance effects that enable sensitive and reversible detection of strain deformations with high linearity and negligible hysteresis. The unique mechanical and sensing properties of the OS² enable its assembling into clothing or mounting on skin surfaces for monitoring various human activities from physiological signals as subtle as wrist pulses to large motions of joint bending and hand gestures. We further apply the OS² for quantitative analysis of motor disorders such as Parkinson's disease and demonstrate its compatibility in strong electromagnetic interference environments during functional magnetic resonance imaging, showing great promises for diagnostics and assessments of motor neuron diseases in clinics.

Detection of Abnormal Segments in Finger Tapping Waveform using One-class SVM

We have developed a finger-tapping device with magnetic sensors, UB2, for the early detection of dementia. The goal of the present study is to develop a method for detecting abnormal segments in the finger tapping waveform in an objective way using machine learning and to evaluate the method in comparison with a human visual assessment. Fifteen-second right-hand finger tapping waveforms of 228 healthy volunteers were measured and cut into one-cycle taps. Fifteen features representing the properties of the one-cycle taps were extracted. As a result of applying a one-class support vector machine (SVM) with an outlier rate of 0.08, 1032 one-cycle taps (8.0%) were detected as abnormal among all 12,898 one-cycle taps. Among these abnormal ones, the features including many outliers (>30%) were the instances of freezing (small fluctuations) and the tap interval. These features correspond to those of which distribution were markedly biased. The visual assessment was likely to overestimate abnormality concerning the instances of freezing and the tap interval (>10%) and conversely underestimate abnormality concerning amplitude of distance/velocity or motion quantity (<; -10%).

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.

Effectiveness of Serious Games for Leap Motion on the Functionality of the Upper Limb in Parkinson's Disease: A Feasibility Study

The design and application of Serious Games (SG) based on the Leap Motion sensor are presented as a tool to support the rehabilitation therapies for upper limbs. Initially, the design principles and their implementation are described, focusing on improving both unilateral and bilateral manual dexterity and coordination. The design of the games has been supervised by specialized therapists. To assess the therapeutic effectiveness of the proposed system, a protocol of trials with Parkinson's patients has been defined. Evaluations of the physical condition of the participants in the study, at the beginning and at the end of the treatment, are carried out using standard tests. The specific measurements of each game give the therapist more detailed information about the patients' evolution after finishing the planned protocol. The obtained results support the fact that the set of developed video games can be combined to define different therapy protocols and that the information obtained is richer than the one obtained through current clinical metrics, serving as method of motor function assessment.

Quantitative assessment of finger tapping characteristics in mild cognitive impairment, Alzheimer's disease, and Parkinson's disease

BACKGROUND

Fine motor impairments are common in neurodegenerative disorders, yet standardized, quantitative measurements of motor abilities are uncommonly used in neurological practice. Thus, understanding and comparing fine motor abilities across disorders have been limited.

OBJECTIVES

The current study compared differences in finger tapping, inter-tap interval, and variability in Alzheimer's disease (AD), Parkinson's disease (PD), mild cognitive impairment (MCI), and healthy older adults (HOA).

METHODS

Finger tapping was measured using a highly sensitive light-diode finger tapper. Total number of finger taps, inter-tap interval, and intra-individual variability (IIV) of finger tapping was measured and compared in AD (n = 131), PD (n = 63), MCI (n = 46), and HOA (n = 62), controlling for age and sex.

RESULTS

All patient groups had fine motor impairments relative to HOA. AD and MCI groups produced fewer taps with longer inter-tap interval and higher IIV compared to HOA. The PD group, however, produced more taps with shorter inter-tap interval and higher IIV compared to HOA.

CONCLUSIONS

Disease-specific changes in fine motor function occur in the most common neurodegenerative diseases. The findings suggest that alterations in finger tapping patterns are common in AD, MCI, and PD. In addition, the present results underscore the importance of motor dysfunction even in neurodegenerative disorders without primary motor symptoms.

Temporal Processing Instability with Millisecond Accuracy is a Cardinal Feature of Sensorimotor Impairments in Autism Spectrum Disorder: Analysis Using the Synchronized Finger-Tapping Task

To identify a specific sensorimotor impairment feature of autism spectrum disorder (ASD), we focused on temporal processing with millisecond accuracy. A synchronized finger-tapping task was used to characterize temporal processing in individuals with ASD as compared to typically developing (TD) individuals. We found that individuals with ASD showed more variability in temporal processing parameters than TD individuals. In addition, temporal processing instability was related to altered motor performance. Further, receiver operating characteristic (ROC) curve analyses indicated that altered temporal processing can be useful for distinguishing between individuals with and without ASD. These results suggest that instability of temporal processing with millisecond accuracy is a fundamental feature of sensorimotor impairments in ASD.