Quantitative Assessment of the Arm/Hand Movements in Parkinson's Disease Using a Wireless Armband Device

Reliability and Discriminative Validity of Wearable Sensors for the Quantification of Upper Limb Movement Disorders in Individuals with Dyskinetic Cerebral Palsy

Background-Movement patterns in dyskinetic cerebral palsy (DCP) are characterized by abnormal postures and involuntary movements. Current evaluation tools in DCP are subjective and time-consuming. Sensors could yield objective information on pathological patterns in DCP, but their reliability has not yet been evaluated. The objectives of this study were to evaluate (i) reliability and (ii) discriminative ability of sensor parameters. Methods-Inertial measurement units were placed on the arm, forearm, and hand of individuals with and without DCP while performing reach-forward, reach-and-grasp-vertical, and reach-sideways tasks. Intra-class correlation coefficients (ICC) were calculated for reliability, and Mann-Whitney U-tests for between-group differences. Results-Twenty-two extremities of individuals with DCP (mean age 16.7 y) and twenty individuals without DCP (mean age 17.2 y) were evaluated. ICC values for all sensor parameters except jerk and sample entropy ranged from 0.50 to 0.98 during reach forwards/sideways and from 0.40 to 0.95 during reach-and-grasp vertical. Jerk and maximal acceleration/angular velocity were significantly higher for the DCP group in comparison with peers. Conclusions-This study was the first to assess the reliability of sensor parameters in individuals with DCP, reporting high between- and within-session reliability for the majority of the sensor parameters. These findings suggest that pathological movements of individuals with DCP can be reliably captured using a selection of sensor parameters.

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.

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease

Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

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

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

Motor Signatures in Digitized Cognitive and Memory Tests Enhances Characterization of Parkinson's Disease

Although interest in using wearable sensors to characterize movement disorders is growing, there is a lack of methodology for developing clinically interpretable biomarkers. Such digital biomarkers would provide a more objective diagnosis, capturing finer degrees of motor deficits, while retaining the information of traditional clinical tests. We aim at digitizing traditional tests of cognitive and memory performance to derive motor biometrics of pen-strokes and voice, thereby complementing clinical tests with objective criteria, while enhancing the overall characterization of Parkinson's disease (PD). 35 participants including patients with PD, healthy young and age-matched controls performed a series of drawing and memory tasks, while their pen movement and voice were digitized. We examined the moment-to-moment variability of time series reflecting the pen speed and voice amplitude. The stochastic signatures of the fluctuations in pen drawing speed and voice amplitude of patients with PD show a higher signal-to-noise ratio compared to those of neurotypical controls. It appears that contact motions of the pen strokes on a tablet evoke sensory feedback for more immediate and predictable control in PD, while voice amplitude loses its neurotypical richness. We offer new standardized data types and analytics to discover the hidden motor aspects within the cognitive and memory clinical assays.

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

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

A non-contact system for the assessment of hand motor tasks in people with Parkinson’s disease

Clinical diagnosis of Parkinson’s disease (PD) motor symptoms remains a problem. Most of the current studies focus on objective evaluations to make the evaluation more reliable. Most of these systems are based on the use of inertial and electromyographic sensors that require contact with the body part being assessed. Contact sensors restrict natural movement, may be uncomfortable and may require preparation of the body, which may cause irritation. As an alternative to contact sensors for the study of hand motor tasks performed by subjects with and without PD, electrical potential sensing technology is used in this research. A custom hardware has been designed to enable data collection by hand movement. A micro-machine system validated the developed system, and a relationship model was established between hand displacement and non-contact capacitive (NCC) sensor response. An experiment was conducted, including 57 subjects, 30 with PD (experimental group) and 27 healthy control group, followed by an analysis of statistical features extracted from the instantaneous mean frequency (IMNF) of NCC sensor. These results were compared with those obtained from gyroscope signals that are considered in the field to be the gold standard. As a result, NCC responses were correlated linearly with hand displacement (R2 = 0.7692 and $${\text{R}}_{\text{adj}}^{2}$$ R adj 2  = 0.7631). The statistical evaluation of IMNF features showed, that both, contact and non-contact sensors, were able to discriminate movement patterns of the control group from the experimental one. The results confirm statistical similarity between features extracted from NCC and gyroscope signals.

What the Tech? The Management of Neurological Dysfunction Through the Use of Digital Technology

Worldwide, it is estimated that millions of individuals suffer from a neurological disorder which can be the result of head injuries, ischaemic events such as a stroke, or neurodegenerative disorders such as Parkinson's disease (PD) and multiple sclerosis (MS). Problems with mobility and hemiparesis are common for these patients, making daily life, social factors and independence heavily affected. Current therapies aimed at improving such conditions are often tedious in nature, with patients often losing vital motivation and positive outlook towards their rehabilitation. The interest in the use of digital technology in neuro-rehabilitation has skyrocketed in the past decade. To gain insight, a systematic review of the literature in the field was conducting following the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines for three categories: stroke, Parkinson's disease and multiple sclerosis. It was found that the majority of the literature (84%) was in favour of the use of digital technologies in the management of neurological dysfunction; with some papers taking a "neutral" or "against" standpoint. It was found that the use of technologies such as virtual reality (VR), robotics, wearable sensors and telehealth was highly accepted by patients, helped to improve function, reduced anxiety and make therapy more accessible to patients living in more remote areas. The most successful therapies were those that used a combination of conventional therapies and new digital technologies.

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.

Quantitative assessment of lower limbs gross motor function in children with cerebral palsy based on surface EMG and inertial sensors

Taking advantage of motion sensing technology, a quantitative assessment method for lower limbs motor function of cerebral palsy (CP) based on the gross motor function measurement (GMFM)-24 scale was explored in this study. According to the motion analysis on GMFM-24 scale, we translated the assessment problem of GMFM-24 scale into a detection problem of different motion modes including static state, fall, step, turning, alternating gait, walking, running, lifting legs, kicking balls, and jumping. The surface electromyography (sEMG) electrodes and inertial sensors were adopted to capture motion data, and a framework integrating a series of detection algorithms was presented for the assessment of lower limbs gross motor function. Two groups of participants including 8 healthy adults and 14 CP children were recruited. A self-developed data acquisition equipment integrating 24 sEMG electrodes and 9 inertial units was adopted for data acquisition. A platform based on two laser beam sensors was used to perform cross-border detection. The parameters/thresholds of motion detection algorithms were determined by the data from healthy adults, and the lower limbs gross motor function evaluation was conducted on 14 CP children. The experimental results verified the feasibility and effectiveness of the proposed quantitative assessment method. Compared to the clinical assessment score based on GMFM-24 scale, 90.1% accuracy was obtained for evaluation of 303 tasks in 14 CP children. The objective motor function assessment method proposed has potential application value for the quantitative assessment of lower limbs motor function of CP children in clinical practice. Graphical abstract The algorithm framework for the assessment of lower limbs gross motor function. Using the GMFM-24 scale as the evaluation standard, a quantitative evaluation program for the lower limbs gross motor function of CP children based on motion sensing technology was proposed.

Acceptability to patients, carers and clinicians of an mHealth platform for the management of Parkinson's disease (PD_Manager): study protocol for a pilot randomised controlled trial

BACKGROUND

Parkinson's disease is a degenerative neurological condition causing multiple motor and non-motor symptoms that have a serious adverse effect on quality of life. Management is problematic due to the variable and fluctuating nature of symptoms, often hourly and daily. The PD_Manager mHealth platform aims to provide a continuous feed of data on symptoms to improve clinical understanding of the status of any individual patient and inform care planning. The objectives of this trial are to (1) assess patient (and family carer) perspectives of PD_Manager regarding comfort, acceptability and ease of use; (2) assess clinician views about the utility of the data generated by PD_Manager for clinical decision making and the acceptability of the system in clinical practice.

METHODS/DESIGN

This trial is an unblinded, parallel, two-group, randomised controlled pilot study. A total of 200 persons with Parkinson's disease (Hoehn and Yahr stage 3, experiencing motor fluctuations at least 2 h per day), with primary family carers, in three countries (110 Rome, 50 Venice, Italy; 20 each in Ioannina, Greece and Surrey, England) will be recruited. Following informed consent, baseline information will be gathered, including the following: age, gender, education, attitudes to technology (patient and carer); time since Parkinson's diagnosis, symptom status and comorbidities (patient only). Randomisation will assign participants (1:1 in each country), to PD_Manager vs control, stratifying by age (1 ≤ 70 : 1 > 70) and gender (60% M: 40% F). The PD_Manager system captures continuous data on motor symptoms, sleep, activity, speech quality and emotional state using wearable devices (wristband, insoles) and a smartphone (with apps) for storing and transmitting the information. Control group participants will be asked to keep a symptom diary covering the same elements as PD_Manager records. After a minimum of two weeks, each participant will attend a consultation with a specialist doctor for review of the data gathered (by either means), and changes to management will be initiated as indicated. Patients, carers and clinicians will be asked for feedback on the acceptability and utility of the data collection methods. The PD_Manager intervention, compared to a symptom diary, will be evaluated in a cost-consequences framework.

DISCUSSION

Information gathered will inform further development of the PD_Manager system and a larger effectiveness trial.

TRIAL REGISTRATION

ISRCTN Registry, ISRCTN17396879 . Registered on 15 March 2017.

Objective assessment of bradykinesia in Parkinson's disease using evolutionary algorithms: clinical validation

BACKGROUND

There is an urgent need for developing objective, effective and convenient measurements to help clinicians accurately identify bradykinesia. The purpose of this study is to evaluate the accuracy of an objective approach assessing bradykinesia in finger tapping (FT) that uses evolutionary algorithms (EAs) and explore whether it can be used to identify early stage Parkinson's disease (PD).

METHODS

One hundred and seven PD, 41 essential tremor (ET) patients and 49 normal controls (NC) were recruited. Participants performed a standard FT task with two electromagnetic tracking sensors attached to the thumb and index finger. Readings from the sensors were transmitted to a tablet computer and subsequently analyzed by using EAs. The output from the device (referred to as "PD-Monitor") scaled from - 1 to + 1 (where higher scores indicate greater severity of bradykinesia). Meanwhile, the bradykinesia was rated clinically using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) FT item.

RESULTS

With an increasing MDS-UPDRS FT score, the PD-Monitor score from the same hand side increased correspondingly. PD-Monitor score correlated well with MDS-UPDRS FT score (right side: r = 0.819, P = 0.000; left side: r = 0.783, P = 0.000). Moreover, PD-Monitor scores in 97 PD patients with MDS-UPDRS FT bradykinesia and each PD subgroup (FT bradykinesia scored from 1 to 3) were all higher than that in NC. Receiver operating characteristic (ROC) curves revealed that PD-Monitor FT scores could detect different severity of bradykinesia with high accuracy (≥89.7%) in the right dominant hand. Furthermore, PD-Monitor scores could discriminate early stage PD from NC, with area under the ROC curve greater than or equal to 0.899. Additionally, ET without bradykinesia could be differentiated from PD by PD-Monitor scores. A positive correlation of PD-Monitor scores with modified Hoehn and Yahr stage was found in the left hand sides.

CONCLUSIONS

Our study demonstrated that a simple to use device employing classifiers derived from EAs could not only be used to accurately measure different severity of bradykinesia in PD, but also had the potential to differentiate early stage PD from normality.

[Application of movement recognition technology in assessing spontaneous general movements in preterm infants]

Preterm birth is a major factor which induces neurological and motor impairments, particularly cerebral palsy, in high-risk infants. Early identification of potential neurodevelopmental impairments provides the opportunity to improve neurodevelopmental outcomes in preterm infants through early rehabilitation interventions. Clinically, the general movement assessment is a pivotal tool to predict neurodevelopmental outcomes, especially motor developmental outcomes, in high-risk infants. Movement recognition can continuously capture relevant limb movements and perform objective and quantitative assessment using computerized approaches. Various methods of recording and analyzing spontaneous general movements for infants at a risk of cerebral palsy have been extensively explored. This article summarizes the general movement assessment method and reviews the translational research on using movement recognition technology for the assessment of spontaneous general movements of preterm infants.

[Application of movement recognition technology in assessing spontaneous general movements in preterm infants]

Preterm birth is a major factor which induces neurological and motor impairments, particularly cerebral palsy, in high-risk infants. Early identification of potential neurodevelopmental impairments provides the opportunity to improve neurodevelopmental outcomes in preterm infants through early rehabilitation interventions. Clinically, the general movement assessment is a pivotal tool to predict neurodevelopmental outcomes, especially motor developmental outcomes, in high-risk infants. Movement recognition can continuously capture relevant limb movements and perform objective and quantitative assessment using computerized approaches. Various methods of recording and analyzing spontaneous general movements for infants at a risk of cerebral palsy have been extensively explored. This article summarizes the general movement assessment method and reviews the translational research on using movement recognition technology for the assessment of spontaneous general movements of preterm infants.