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

BACKGROUND: Clinical rating of bradykinesia in Parkinson disease (PD) is challenging as it must combine several movement features into a single score. Additionally, in-clinic assessment cannot capture fluctuations throughout the day. OBJECTIVE: To evaluate the reliability and responsiveness of a motion sensor-based tablet app for objective bradykinesia assessment in clinic and at home as compared to clinical ratings. METHODS: Thirty-two PD patients treated with subthalamic deep brain stimulation (DBS) were outfitted with a motion sensor on the index finger of the more affected hand to perform two repetitions of finger-tapping, hand opening-closing, and arm pronation-supination tasks with DBS on and 10, 20, and 30 minutes after turning DBS off. Tasks were videotaped for blinded clinician rating using the Modified Bradykinesia Rating Scale (MBRS). Participants were then sent home with an app-based system to perform two repetitions of the same tasks six times per day spaced two hours apart, three days per week, for two weeks. Intraclass correlation (ICC) and minimal detectable change (MDC) were calculated. RESULTS: As the effects of DBS wore off, motion sensors detected worsening of amplitude sooner than did clinician-rated MBRS for all three tasks. ICCs were significantly higher and MDCs were significantly lower for motion sensors in the clinic and at home than for clinician ratings (p < 0.01). CONCLUSIONS: The tablet-based app demonstrated higher reliability and responsiveness in capturing bradykinesia-related tasks in the clinic and at home than did clinician ratings. This tool may enhance the assessment of novel therapies.

Telehealth Management of Parkinson's Disease Using Wearable Sensors: An Exploratory Study.

BACKGROUND: Parkinson's disease (PD) motor symptoms can fluctuate and may not be accurately reflected during a clinical evaluation. In addition, access to movement disorder specialists is limited for many with PD. The objective was to assess the impact of motion sensor-based telehealth diagnostics on PD clinical care and management. METHODS: Eighteen adults with PD were randomized to control or experimental groups. All participants were instructed to use a motion sensor-based monitoring system at home one day per week, for seven months. The system included a finger-worn motion sensor and tablet-based software interface that guided patients through tasks to quantify tremor, bradykinesia, and dyskinesia. Data were processed into motor symptom severity reports, which were reviewed by a movement disorders neurologist for experimental group participants. After three months and six months, control group participants visited the clinic for a routine appointment, while experimental group participants had a videoconference or phone call instead. RESULTS: Home based assessments were completed with median compliance of 95.7%. For a subset of participants, the neurologist successfully used information in the reports such as quantified response to treatment or progression over time to make therapy adjustments. Changes in clinical characteristics from study start to end were not significantly different between groups. DISCUSSION: Individuals with PD were able and willing to use remote monitoring technology. Patient management aided by telehealth diagnostics provided comparable outcomes to standard care. Telehealth technologies combined with wearable sensors have the potential to improve care for disparate PD populations or those unable to travel.

Wearable Sensors for Advanced Therapy Referral in Parkinson's Disease.

BACKGROUND: Advanced therapies, such as deep brain stimulation and levodopa-carbidopa intestinal gel, can significantly improve quality of life in advanced Parkinson's disease (PD). However, determining who should be referred for advanced therapy is a challenging problem. OBJECTIVE: The objective was to determine the impact of remote monitoring using objective, wearable sensors on the advanced therapy referral rate in patients with advanced PD and if sensor data differed in patients who were referred and those who were not. METHODS: A retrospective, exploratory, secondary analysis was performed on data collected in a study that followed forty individuals with advanced PD for one year with half receiving standard care and half using motion sensor-based remote monitoring once per month in conjunction with standard care. Advanced therapy referral rates were compared between groups. For the group who underwent remote monitoring, objective motor features representing symptoms, dyskinesias, and fluctuations were examined to determine if objective kinematic features differed between patients who were and were not recommended for advanced therapy. RESULTS: The advanced therapy referral rate was significantly higher for patients when a clinician had access to remote monitoring reports compared to standard care alone (63.6% versus 11.8%, p < 0.01). Bradykinesia severity, bradykinesia fluctuations, and dyskinesia severity differed significantly (p < 10e-8, p < 10e-5, and p < 0.01, respectively) between patients recommended and not recommended for advanced therapy. CONCLUSIONS: Remote monitoring technologies can capture motor features that may be clinically useful in identifying patients who may be candidates for advanced therapy. This could lead to development of automated screening algorithms, improve referral efficiency, and expand access to advanced therapies for patients with advanced PD.

Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease.

OBJECTIVE: Pilot study to evaluate computer-guided deep brain stimulation (DBS) programming designed to optimize stimulation settings using objective motion sensor-based motor assessments. MATERIALS AND METHODS: Seven subjects (five males; 54-71 years) with Parkinson's disease (PD) and recently implanted DBS systems participated in this pilot study. Within two months of lead implantation, the subject returned to the clinic to undergo computer-guided programming and parameter selection. A motion sensor was placed on the index finger of the more affected hand. Software guided a monopolar survey during which monopolar stimulation on each contact was iteratively increased followed by an automated assessment of tremor and bradykinesia. After completing assessments at each setting, a software algorithm determined stimulation settings designed to minimize symptom severities, side effects, and battery usage. RESULTS: Optimal DBS settings were chosen based on average severity of motor symptoms measured by the motion sensor. Settings chosen by the software algorithm identified a therapeutic window and improved tremor and bradykinesia by an average of 35.7% compared with baseline in the "off" state (p < 0.01). CONCLUSIONS: Motion sensor-based computer-guided DBS programming identified stimulation parameters that significantly improved tremor and bradykinesia with minimal clinician involvement. Automated motion sensor-based mapping is worthy of further investigation and may one day serve to extend programming to populations without access to specialized DBS centers.

Motion sensor strategies for automated optimization of deep brain stimulation in Parkinson's disease.

BACKGROUND: Deep brain stimulation (DBS) is a well-established treatment for Parkinson's disease (PD). Optimization of DBS settings can be a challenge due to the number of variables that must be considered, including presence of multiple motor signs, side effects, and battery life. METHODS: Nine PD subjects visited the clinic for programming at approximately 1, 2, and 4 months post-surgery. During each session, various stimulation settings were assessed and subjects performed motor tasks while wearing a motion sensor to quantify tremor and bradykinesia. At the end of each session, a clinician determined final stimulation settings using standard practices. Sensor-based ratings of motor symptom severities collected during programming were then used to develop two automated programming algorithms--one to optimize symptom benefit and another to optimize battery life. Therapeutic benefit was compared between the final clinician-determined DBS settings and those calculated by the automated algorithm. RESULTS: Settings determined using the symptom optimization algorithm would have reduced motor symptoms by an additional 13 percentage points when compared to clinician settings, typically at the expense of increased stimulation amplitude. By adding a battery life constraint, the algorithm would have been able to decrease stimulation amplitude by an average of 50% while maintaining the level of therapeutic benefit observed using clinician settings for a subset of programming sessions. CONCLUSIONS: Objective assessment in DBS programming can identify settings that improve symptoms or obtain similar benefit as clinicians with improvement in battery life. Both options have the potential to improve post-operative patient outcomes.

Motion sensor dyskinesia assessment during activities of daily living.

BACKGROUND: Dyskinesia throughout the levodopa dose cycle has been previously measured in patients with Parkinson's disease (PD) using a wrist-worn motion sensor during the stationary tasks of arms resting and extended. Quantifying dyskinesia during unconstrained activities poses a unique challenge since these involuntary movements are kinematically similar to voluntary movement. OBJECTIVE: To determine the feasibility of using motion sensors to measure dyskinesia during activities of daily living. METHODS: Fifteen PD subjects performed scripted activities of daily living while wearing motion sensors on bilateral hands, thighs, and ankles over the course of a levodopa dose cycle. Videos were scored by clinicians using the modified Abnormal Involuntary Movement Scale to rate dyskinesia severity in separate body regions, with the total score used as an overall measure. Kinematic features were extracted from the motion data and algorithms were generated to output severity scores. RESULTS: Movements when subjects were experiencing dyskinesia were less smooth than when they were not experiencing dyskinesia. Dyskinesia scores predicted by the model using all sensors were highly correlated with clinician scores, with a correlation coefficient of 0.86 and normalized root-mean-square-error of 7.4%. Accurate predictions were maintained when two sensors on the most affected side of the body (one on the upper extremity and one on the lower extremity) were used. CONCLUSIONS: A system with motion sensors may provide an accurate measure of overall dyskinesia that can be used to monitor patients as they complete typical activities, and thus provide insight on symptom fluctuation in the context of daily life.

Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson's disease.

BACKGROUND: Enhancing the reliability and responsiveness of motor assessments required to demonstrate therapeutic efficacy is a priority for Parkinson's disease (PD) clinical trials. The objective of this study is to determine the reliability and responsiveness of a portable kinematic system for quantifying PD motor deficits as compared to clinical ratings. METHODS: Eighteen PD patients with subthalamic nucleus deep-brain stimulation (DBS) performed three tasks for evaluating resting tremor, postural tremor, and finger-tapping speed, amplitude, and rhythm while wearing a wireless motion-sensor unit (Kinesia) on the more-affected index finger. These tasks were repeated three times with DBS turned off and at each of 10 different stimulation amplitudes chosen to yield small changes in treatment response. Each task performance was video-recorded for subsequent clinician rating in blinded, randomized order. Test-retest reliability was calculated as intraclass correlation (ICC) and sensitivity was calculated as minimal detectable change (MDC) for each DBS amplitude. RESULTS: ICCs for Kinesia were significantly higher than those for clinician ratings of finger-tapping speed (p < 0.0001), amplitude (p < 0.0001), and rhythm (p < 0.05), but were not significantly different for evaluations of resting or postural tremor. Similarly, Kinesia scores yielded a lower MDC as compared with clinician scores across all finger-tapping subscores (p < 0.0001), but did not differ significantly for resting and postural tremor. CONCLUSIONS: The Kinesia portable kinematic system can provide greater test-retest reliability and sensitivity to change than conventional clinical ratings for measuring bradykinesia, hypokinesia, and dysrhythmia in PD patients.

Development of a clinician worn device for the evaluation of abnormal muscle tone.

Neurological disorders such as cerebral palsy commonly result in abnormal muscle hyperactivity that negatively effects functional use of the affected limbs. Individuals with cerebral palsy often present with a mix of spasticity and dystonia, and it can be difficult to distinguish between the effects of these types of abnormal tone. Different types of abnormal tone respond differently to treatments such as deep brain stimulation and baclofen. Conventional clinical evaluation techniques provide minimal information for distinguishing abnormal tone characteristics and changes from treatment. Devices that quantify abnormal tone characteristics can help distinguish between the effects of different types of abnormal muscle tone, and help to quantify treatment effects. This paper discusses the development and initial evaluation of MyoSense(TM), a clinician worn device for the quantification and differentiation of abnormal muscle tone. MyoSense evaluates the orientation, speed, and force during clinician manipulation of the affected limbs with a protocol that is similar to conventional practice for evaluating abnormal tone. Evaluation of the MyoSense device, using a mechanical apparatus to simulate abnormal muscle tone, showed good resolution of abnormal tone characteristics. Using a procedure directly modeled after conventional clinical evaluation of abnormal tone, MyoSense data showed good correlation with simulated profiles, 0.8 for spasticity and 0.93 for hypertonia. Evaluation of average change across different limb manipulation speeds, to mitigate acceleration and mechanical effects, resulted in MyoSense data correlations to simulated profiles of 0.99 for spasticity, spasticity with a catch, and dystonia. Overall these results show promise for future clinical evaluation of the MyoSense device.

Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson's disease.

BACKGROUND: Chronic use of medication for treating Parkinson's disease (PD) can give rise to peak-dose dyskinesia. Adjustments in medication often sacrifice control of motor symptoms, and thus balancing this trade-off poses a significant challenge for disease management. OBJECTIVE: To determine whether a wrist-worn motion sensor unit could be used to ascertain global dyskinesia severity over a levodopa dose cycle and to develop a severity scoring algorithm highly correlated with clinician ratings. METHODS: Fifteen individuals with PD were instrumented with a wrist-worn motion sensor unit, and data were collected with arms in resting and extended positions once every hour for three hours after taking a levodopa dose. Two neurologists blinded to treatment status viewed subject videos and rated global and upper extremity dyskinesia severity based on the modified Abnormal Involuntary Movement Scale (mAIMS). Linear regression models were developed using kinematic features extracted from motion sensor data and extremity, global, or combined (average of extremity and global) mAIMS scores. RESULTS: Dyskinesia occurring during a levodopa dose cycle was successfully measured using a wrist-worn sensor. The logarithm of the power spectrum area between 0.3-3 Hz and the combined clinician scores resulted in the best model performance, with a correlation coefficient between clinician and model scores of 0.81 and root mean square error of 0.55, both averaged across the arms resting and extended postures. CONCLUSIONS: One sensor unit worn on either hand can effectively predict global dyskinesia severity during the arms resting or extended positions.

Quantitative analysis of gait and balance response to deep brain stimulation in Parkinson's disease.

Gait and balance disturbances in Parkinson's disease (PD) can be debilitating and may lead to increased fall risk. Deep brain stimulation (DBS) is a treatment option once therapeutic benefits from medication are limited due to motor fluctuations and dyskinesia. Optimizing DBS parameters for gait and balance can be significantly more challenging than for other PD motor symptoms. Furthermore, inter-rater reliability of the standard clinical PD assessment scale, Unified Parkinson's Disease Rating Scale (UPDRS), may introduce bias and washout important features of gait and balance that may respond differently to PD therapies. Study objectives were to evaluate clinician UPDRS gait and balance scoring inter-rater reliability, UPDRS sensitivity to different aspects of gait and balance, and how kinematic features extracted from motion sensor data respond to stimulation. Forty-two subjects diagnosed with PD were recruited with varying degrees of gait and balance impairment. All subjects had been prescribed dopaminergic medication, and 20 subjects had previously undergone DBS surgery. Subjects performed seven items of the gait and balance subset of the UPDRS while wearing motion sensors on the sternum and each heel and thigh. Inter-rater reliability varied by UPDRS item. Correlation coefficients between at least one kinematic feature and corresponding UPDRS scores were greater than 0.75 for six of the seven items. Kinematic features improved (p<0.05) from DBS-OFF to DBS-ON for three UPDRS items. Despite achieving high correlations with the UPDRS, evaluating individual kinematic features may help address inter-rater reliability issues and rater bias associated with focusing on different aspects of a motor task.

Amplitude fluctuations in essential tremor.

OBJECTIVE: To assess temporal amplitude variability in patients with essential tremor (ET). METHODS: Patients who satisfied the diagnostic criteria for probable or definite ET were enrolled in the study. Each enrolled patient was first rated using the essential tremor rating assessment scale (TETRAS). Postural and kinetic tremors of the arms were then measured using a quantitative motor assessment system (QMAS) starting at 8:00 AM (T0-baseline) every 2 h for 6 h. Subjects were videotaped performing the tasks. Single subjects consecutively performed each assessment twice during every time-interval. At the end of the study, videos were randomized and blindly rated using TETRAS. RESULTS: Twelve ET subjects were enrolled. QMAS and video scores were directly correlated with high test-retest reliability for each time-interval. Furthermore, the QMAS scores at T0 significantly correlated with in-person rated TETRAS scores as well as with subsequent time-intervals instrumental scores. No significant differences were detected between time-intervals QMAS average measurements using ANOVA. There was a maximal 23% absolute variation in tremor amplitude from baseline as determined by the QMAS. Test for equality of variance showed high measurement variability for subjects with high QMAS scores at T0 and throughout the 6 h of assessment. CONCLUSIONS: Baseline measures are predictive of tremor amplitude at subsequent assessments during the day. High amplitude tremor is associated with high intra-assessment variability.

Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology.

The objective was to capture levodopa-induced dyskinesia (LID) in patients with Parkinson's disease (PD) using body-worn motion sensors. Dopaminergic treatment in PD can induce abnormal involuntary movements, including choreatic dyskinesia (brief, rapid, irregular movements). Adjustments in medication to reduce LID often sacrifice control of motor symptoms, and balancing this tradeoff poses a significant challenge for management of advanced PD. Fifteen PD subjects with known LID were recruited and instructed to perform two stationary motor tasks while wearing a compact wireless motion sensor unit positioned on each hand over the course of a levodopa dose cycle. Videos of subjects performing the motor tasks were later scored by expert clinicians to assess global dyskinesia using the modified Abnormal Involuntary Rating Scale (m-AIMS). Kinematic features were extracted from motion data in different frequency bands (1-3Hz and 3-8Hz) to quantify LID severity and to distinguish between LID and PD tremor. Receiver operator characteristic analysis was used to determine thresholds for individual features to detect the presence of LID. A sensitivity of 0.73 and specificity of 1.00 were achieved. A neural network was also trained to output dyskinesia severity on a 0 to 4 scale, similar to the m-AIMS. The model generalized well to new data (coefficient of determination= 0.85 and mean squared error= 0.3). This study demonstrated that hand-worn motion sensors can be used to assess global dyskinesia severity independent of PD tremor over the levodopa dose cycle.

Automated motion sensor quantification of gait and lower extremity bradykinesia.

The objective was to develop and evaluate algorithms for quantifying gait and lower extremity bradykinesia in patients with Parkinson's disease using kinematic data recorded on a heel-worn motion sensor unit. Subjects were evaluated by three movement disorder neurologists on four domains taken from the Movement Disorders Society Unified Parkinson's Disease Rating Scale while wearing the motion sensor unit. Multiple linear regression models were developed based on the recorded kinematic data and clinician scores and produced outputs highly correlated to clinician scores with an average correlation coefficient of 0.86. The newly developed models have been integrated into a home-based system for monitoring Parkinson's disease motor symptoms.

Feasibility of home-based automated Parkinson's disease motor assessment.

Patients with Parkinson's disease (PD) receive therapies aimed at addressing a diverse range of motor symptoms. Motor complications in the form of symptom fluctuations and dyskinesias that commonly occur with chronic PD medication use may not be effectively captured by Unified Parkinson's Disease Rating Scale (UPDRS) assessments performed in the clinic. Therefore, home monitoring may be a viable adjunct tool to provide insight into PD motor symptom response to treatment. In this pilot study, we sought to evaluate the feasibility of capturing PD motor symptoms at home using a computer-based assessment system. Ten subjects diagnosed with idiopathic PD used the system at home and ten non-PD control subjects used the system in a laboratory. The Kinesia system consists of a wireless finger-worn motion sensor and a laptop computer with software for automated tremor and bradykinesia severity score assessments. Data from control subjects were used to develop compliance algorithms for rejecting motor tasks performed incorrectly. These algorithms were then applied to data collected from the PD subjects who used the Kinesia system at home to complete motor exams 3-6 times per day over 3-6 days. Motor tasks not rejected by the compliance algorithms were further processed for symptom severity. PD subjects successfully completed motor assessments at home, with approximately 97% of all motor task data files (1222/1260) accepted. These findings suggest that objective home monitoring of PD motor fluctuations is feasible.

The modified bradykinesia rating scale for Parkinson's disease: reliability and comparison with kinematic measures.

Bradykinesia encompasses slowness, decreased movement amplitude, and dysrhythmia. Unified Parkinson's Disease Rating Scale-based bradykinesia-related items require that clinicians condense abnormalities in speed, amplitude, fatiguing, hesitations, and arrests into a single score. The objective of this study was to evaluate the reliability of a modified bradykinesia rating scale, which separately assesses speed, amplitude, and rhythm and its correlation with kinematic measures from motion sensors. Fifty patients with Parkinson's disease performed Unified Parkinson's Disease Rating Scale-directed finger tapping, hand grasping, and pronation-supination while wearing motion sensors. Videos were rated blindly and independently by 4 clinicians. The modified bradykinesia rating scale and Unified Parkinson's Disease Rating Scale demonstrated similar inter- and intrarater reliability. Raters placed greater weight on amplitude than on speed or rhythm when assigning a Unified Parkinson's Disease Rating Scale score. Modified bradykinesia rating scale scores for speed, amplitude, and rhythm correlated highly with quantitative kinematic variables. The modified bradykinesia rating scale separately captures bradykinesia components with interrater and intrarater reliability similar to that of the Unified Parkinson's Disease Rating Scale. Kinematic sensors can accurately quantify speed, amplitude, and rhythm to aid in the development and evaluation of novel therapies in Parkinson's disease.

Essential tremor quantification during activities of daily living.

BACKGROUND: Essential tremor (ET), characterized primarily by postural and kinetic tremor, is typically measured in the clinic with subjective tremor rating scales. These ratings are often used to adjust medications and assess efficacy in clinical trials. However, tremor ratings require the presence of a clinician and do not necessarily capture tremor fluctuations throughout the day during activities of daily living (ADL). OBJECTIVE: To evaluate the ability of motion sensors to discriminate tremor from voluntary posture and motion, classify tremor as postural or kinetic, and rate tremor severity during standardized tasks and non-standardized activities of daily living. METHODS: Ten subjects with ET wore motion sensors on the index finger and performed standardized motor tasks from the Washington Heights-Inwood Genetic Study of Essential Tremor (WHIGET) tremor rating scale (wTRS) and non-standardized ADL tasks. Four movement disorder specialists independently rated video segments of the standardized tasks but not the ADL tasks. Quantitative features were extracted from the motion sensors and used to develop mathematical models for predicting rating scores from kinematic data. RESULTS: The quantitative motion features were highly correlated with wTRS ratings for postural (r = 0.90) and kinetic (r = 0.80) tremors. Mathematical models produced tremor ratings that correlated strongly with clinician ratings of the wTRS tasks (mean r = 0.80) and also produced ADL task ratings that correlated well with the most recent clinician wTRS ratings (mean r = 0.72). CONCLUSIONS: Recordings from motion sensors can be used to classify tremor as postural or kinetic and quantify tremor severity during both standardized and non-standardized activities.

Kinematic optimization of deep brain stimulation across multiple motor symptoms in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms including tremor and bradykinesia (slowness of movement). Drug treatment, although capable of controlling these symptoms over a number of years, becomes less effective as the disease progresses and leads to motor complications such as drug-induced dyskinesia (involuntary abnormal movements). Deep brain stimulation (DBS) provides an alternative means of controlling motor symptoms in these patients, and while DBS has been effective in improving motor symptoms, these improvements are largely based on accurate placement of the lead and the ability of medical personnel to adequately program the DBS device following implantation. While guidelines exist for DBS programming, selection of stimulation parameters and patient outcome is greatly dependent on subjective clinical assessments and the experience of the medical personnel performing the programming. The aim of this project was to assess the feasibility of using a quantitative and objective approach to programming. Two subjects underwent standard procedures for DBS programming while wearing a small, compact motion sensor. Kinematic data were collected from subjects as they completed motor tasks to evaluate DBS efficacy. Quantitative variables characterizing tremor and bradykinesia were related to stimulation parameters. Results indicated different stimulation settings might be required for optimal improvement of different motor symptoms. A standardized method of programming DBS parameters utilizing motion analysis may provide an objective method of assessment that the programmer can use to better identify stimulation parameters to achieve optimal improvement across multiple motor symptoms.

Correlation between Kinesia system assessments and clinical tremor scores in patients with essential tremor.

The primary aim of this study was to determine whether scores on The Essential Tremor Rating Assessment Scale (TETRAS) correlate with quantitative assessments using the Kinesia™ (CleveMed) system in patients with essential tremor (ET). Patients sequentially evaluated and diagnosed with ET at the Parkinson's Disease Center and Movement Disorders Clinic, Baylor College of Medicine were enrolled in the study. The Kinesia portable device was attached to the wrist and subjects were instructed to hold their arms in an outstretched position and then touch their nose while data were wirelessly transmitted to a computer. Subjects were rated on the arm where the system was placed using specific TETRAS items. A linear regression model was constructed for each task using the logarithmic values of both clinical scores and objective motion data parameters to compute a Kinesia score. Twenty subjects underwent complete clinical TETRAS and Kinesia quantitative assessments. TETRAS clinical scores significantly correlated with predicted Kinesia quantitative variables for postural (r = 0.738; P < 0.001) and kinetic (r = 0.57; P = 0.009) tremor. We conclude that the Kinesia system may, therefore, have a utility in quantitative assessments of ET when combined with standard clinical assessment.

Clinically deployable Kinesia technology for automated tremor assessment.

The objective was to design, build, and assess Kinesia, a wireless system for automated assessment of Parkinson's disease (PD) tremor. The current standard in evaluating PD is the Unified Parkinson's Disease Rating Scale (UPDRS), a qualitative ranking system typically completed during an office visit. Kinesia integrates accelerometers and gyroscopes in a compact patient-worn unit to capture kinematic movement disorder features. Objectively quantifying PD manifestations with increased time resolution should aid in evaluating efficacy of treatment protocols and improve patient management. In this study, PD subjects performed the tremor subset of the UPDRS motor section while wearing Kinesia. Quantitative kinematic features were processed and highly correlated to clinician scores for rest tremor (r(2) = 0.89), postural tremor (r(2) = 0.90), and kinetic tremor (r(2) = 0.69). The quantitative features were used to develop a mathematical model that predicted tremor severity scores for new data with low errors. Finally, PD subjects indicated high clinical acceptance.

Upper-extremity stroke therapy task discrimination using motion sensors and electromyography.

Brain injury resulting from stroke often causes upper-extremity motor deficits that limit activities of daily living. Several therapies being developed for motor rehabilitation after stroke focus on increasing time spent using the extremity to promote motor relearning. Providing a novel system for user-worn therapy may increase the amount and rate of functional motor recovery. A user-worn system comprising accelerometers, gyroscopes, and electromyography amplifiers was used to wirelessly transmit motion and muscle activity from normal and stroke subjects to a computer as they completed five upper-extremity rehabilitation tasks. An algorithm was developed to automatically detect the therapy task a subject performed based on the gyroscope and electromyography data. The system classified which task a subject was attempting to perform with greater than 80% accuracy despite the fact that those with severe impairment produced movements that did not resemble the goal tasks and were visually indistinguishable from different tasks. This developed system could potentially be used for home-therapy compliance monitoring, real-time patient feedback and to control therapy interventions.