Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

Contactless finger tapping detection at C band

The rapid finger tap test is widely used in clinical assessment of dyskinesias in Parkinson’s disease. In clinical practice, doctors rely on their clinical experience and use the Parkinson’s Disease Uniform Rating Scale to make a brief judgment of symptoms. We propose a novel C-band microwave sensing method to evaluate finger tapping quantitatively and qualitatively in a non-contact way based on wireless channel information (WCI). The phase difference between adjacent antennas is used to calibrate the original random phase. Outlier filtering and smoothing filtering are used to process WCI waveforms. Based on the resulting signal, we define and extract a set of features related to the features described in UPDRS. Finally, the features are input into a support vector machine (SVM) to obtain results for patients with different severity. The results show that the proposed system can achieve an average accuracy of 99%. Compared with the amplitude, the average quantization accuracy of the phase difference on finger tapping is improved by 3%. In the future, the proposed system could assist doctors to quantify the movement disorders of patients, and it is very promising to be a candidate for clinical practice

Objective assessment of upper limb motor symptoms in Parkinson's Disease using body-worn sensors

MD ThesisBackground There is a need for an objective method of symptom assessment in Parkinson's disease (PD) to enable better treatment decisions and to aid evaluation of new treatments. Current assessment methods; patient-completed symptom diaries and clinical rating scales, have limitations. Accelerometers (sensors capable of capturing data on human movement) and analysis using artificial neural networks (ANNs) have shown potential as a method of motor symptom evaluation in PD. It is unknown whether symptom monitoring with body-worn sensors is acceptable to PD patients due to a lack of previous research. Methods 34 participants with PD wore bilateral wrist-worn accelerometers for 4 hours in a research facility (phase 1) and then for 7 days in their homes (phase 2) whilst also completing symptom diaries. An ANN designed to predict a patient’s motor status, was developed and trained based on accelerometer data during phase 2. ANN performance was evaluated (leave-one-out approach) against patient-completed symptom diaries during phase 2, and against clinician rating of disease state during phase 1 observations. Participants’ views regarding the sensors were obtained via a Likert-style questionnaire completed after each phase. Differences in responses between phases were assessed for using the Wilcoxon rank-sum test. Results ANN-derived values of the proportion of time in each disease state (phase 2), showed strong, significant correlations with values derived from patient-completed symptom diaries. ANN disease state recognition during phase 1 was sub-optimal. High concordance with sensors was seen. Prolonged wearing of the sensors did not adversely affect participants’ opinions on the wearability of the sensors, when compared to their responses following phase 1 Conclusions Accelerometers and ANNs produced results comparable to those of symptom diaries. Our findings suggest that long-term monitoring with wrist-worn sensors is acceptable to PD patients

Parkinson\u27s Symptoms quantification using wearable sensors

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, which leads to false assumption of the disease severity. The objective of this study is to overcome this limitations by building a system using Inertial Measurement Unit (IMU) that can be used at clinics and in home to collect PD symptoms data and build algorithms that can quantify PD symptoms more effectively. Data was acquired from patients seen at movement disorders Clinic at Sanford Health in Fargo, ND. Subjects wore Physilog IMUs and performed tasks for tremor, bradykinesia and gait according to the protocol approved by Sanford IRB. The data was analyzed using modified algorithm that was initially developed using data from normal subjects emulating PD symptoms. For tremor measurement, the study showed that sensor signals collected from the index finger more accurately predict tremor severity compared to signals from a sensor placed on the wrist. For finger tapping, a task measuring bradykinesia, the algorithm could predict with more than 80% accuracy when a set of features were selected to train the prediction model. Regarding gait, three different analysis were done to find the effective parameters indicative of severity of PD. Gait speed measurement algorithm was first developed using treadmill as a reference. Then, it was shown that the features selected could predict PD gait with 85.5% accuracy

Objective evaluation of Parkinson's disease bradykinesia

Bradykinesia is the fundamental motor feature of Parkinson’s disease - obligatory for diagnosis and central to monitoring. It is a complex clinicalsign that describes movements with slow speed, small amplitude, irregular rhythm, brief pauses and progressive decrements. Clinical ascertainment of the presence and severity of bradykinesia relies on subjective interpretation of these components, with considerable variability amongst clinicians, and this may contribute to diagnostic error and inaccurate monitoring in Parkinson’s disease. The primary aim of this thesis was to assess whether a novel non-invasive device could objectively measure bradykinesia and predict diagnostic classification of movement data from Parkinson’s disease patients and healthy controls. The second aim was to evaluate how objective measures of bradykinesia correlate with clinical measures of bradykinesia severity. The third aim was to investigate the characteristic kinematic features of bradykinesia. Forty-nine patients with Parkinson’s disease and 41 healthy controls were recruited in Leeds. They performed a repetitive finger-tapping task for 30 seconds whilst wearing small electromagnetic tracking sensors on their finger and thumb. Movement data was analysed using two different methods - statistical measures of the separable components of bradykinesia and a computer science technique called evolutionary algorithms. Validation data collected independently from 13 patients and nine healthy controls in San Francisco was used to assess whether the results generalised. The evolutionary algorithm technique was slightly superior at classifying the movement data into the correct diagnostic groups, especially for the mildest clinical grades of bradykinesia, and they generalised to the independent group data. The objective measures of finger tapping correlated well with clinical grades of bradykinesia severity. Detailed analysis of the data suggests that a defining feature of Parkinson’s disease bradykinesia called the sequence effect may be a physiological rather than a pathological phenomenon. The results inform the development of a device that may support clinical diagnosis and monitoring of Parkinson’s disease and also be used to investigate bradykinesia

Technological advances in deep brain stimulation:Towards an adaptive therapy

Parkinson's disease (PD) is neurodegenerative movement disorder and a treatment method called deep brain stimulation (DBS) may considerably reduce the patient’s motor symptoms. The clinical procedure involves the implantation of a DBS lead, consisting of multiple electrode contacts, through which continuous high frequency (around 130 Hz) electric pulses are delivered in the brain. In this thesis, I presented the research which had the goal to improve current DBS technology, focusing on bringing the conventional DBS system a step closer to adaptive DBS, a personalized DBS therapy. The chapters in this thesis can be seen as individual building blocks for such an adaptive DBS system. After the general introduction, the first two chapters, two novel DBS lead designs are studied in a computational model. The model showed that both studied leads were able to exploit the novel distribution of the electrode contacts to shape and steer the stimulation field to activate more neurons in the chosen target compared to the conventional lead, and to counteract lead displacement. In the fourth chapter, an inverse current source density (CSD) method is applied on local field potentials (LFP) measured in a rat model. The pattern of CSD sources can act as a landmark within the STN to locate the potential stimulation target. The fifth and final chapter described the last building block of the DBS system. We introduced an inertial sensors and force sensor based measurement system, which can record hand kinematics and joint stiffness of PD patients. A system which can act as a feedback signal in an adaptive DBS system

Low-Cost Objective Measurement of Prehension Skills

This thesis aims to explore the feasibility of using low-cost, portable motion capture tools for the quantitative assessment of sequential 'reach-to-grasp' and repetitive 'finger-tapping' movements in neurologically intact and deficit populations, both in clinical and non-clinical settings. The research extends the capabilities of an existing optoelectronic postural sway assessment tool (PSAT) into a more general Boxed Infrared Gross Kinematic Assessment Tool (BIGKAT) to evaluate prehensile control of hand movements outside the laboratory environment. The contributions of this work include the validation of BIGKAT against a high-end motion capture system (Optotrak) for accuracy and precision in tracking kinematic data. BIGKAT was subsequently applied to kinematically resolve prehensile movements, where concurrent recordings with Optotrak demonstrate similar statistically significant results for five kinematic measures, two spatial measures (Maximum Grip Aperture – MGA, Peak Velocity – PV) and three temporal measures (Movement Time – MT, Time to MGA – TMGA, Time to PV – TPV). Regression analysis further establishes a strong relationship between BIGKAT and Optotrak, with nearly unity slope and low y-intercept values. Results showed reliable performance of BIGKAT and its ability to produce similar statistically significant results as Optotrak. BIGKAT was also applied to quantitatively assess bradykinesia in Parkinson's patients during finger-tapping movements. The system demonstrated significant differences between PD patients and healthy controls in key kinematic measures, paving the way for potential clinical applications. The study characterized kinematic differences in prehensile control in different sensory environments using a Virtual Reality head mounted display and finger tracking system (the Leap Motion), emphasizing the importance of sensory information during hand movements. This highlighted the role of hand vision and haptic feedback during initial and final phases of prehensile movement trajectory. The research also explored marker-less pose estimation using deep learning tools, specifically DeepLabCut (DLC), for reach-to-grasp tracking. Despite challenges posed by COVID-19 limitations on data collection, the study showed promise in scaling reaching and grasping components but highlighted the need for diverse datasets to resolve kinematic differences accurately. To facilitate the assessment of prehension activities, an Event Detection Tool (EDT) was developed, providing temporal measures for reaction time, reaching time, transport time, and movement time during object grasping and manipulation. Though initial pilot data was limited, the EDT holds potential for insights into disease progression and movement disorder severity. Overall, this work contributes to the advancement of low-cost, portable solutions for quantitatively assessing upper-limb movements, demonstrating the potential for wider clinical use and guiding future research in the field of human movement analysis

Technology in Parkinson's disease:challenges and opportunities

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society

Big Data in Parkinson’s Disease: Using Smartphones to Remotely Detect Longitudinal Disease Phenotypes

Objective: To better understand the longitudinal characteristics of Parkinson's disease (PD) through the analysis of finger tapping and memory tests collected remotely using smartphones. Approach: Using a large cohort (312 PD subjects and 236 controls) of participants in the mPower study, we extract clinically validated features from a finger tapping and memory test to monitor the longitudinal behaviour of study participants. We investigate any discrepancy in learning rates associated with motor and non-motor tasks between PD subjects and healthy controls. The ability of these features to predict self-assigned severity measures is assessed whilst simultaneously inspecting the severity scoring system for floor-ceiling effects. Finally, we study the relationship between motor and non-motor longitudinal behaviour to determine if separate aspects of the disease are dependent on one another. Main results: We find that the test performances of the most severe subjects show significant correlations with self-assigned severity measures. Interestingly, less severe subjects do not show significant correlations, which is shown to be a consequence of floor-ceiling effects within the mPower self-reporting severity system. We find that motor performance after practise is a better predictor of severity than baseline performance suggesting that starting performance at a new motor task is less representative of disease severity than the performance after the test has been learnt. We find PD subjects show significant impairments in motor ability as assessed through the alternating finger tapping (AFT) test in both the short- and long-term analyses. In the AFT and memory tests we demonstrate that PD subjects show a larger degree of longitudinal performance variability in addition to requiring more instances of a test to reach a steady state performance than healthy subjects. Significance: Our findings pave the way forward for objective assessment and quantification of longitudinal learning rates in PD. This can be particularly useful for symptom monitoring and assessing medication response. This study tries to tackle some of the major challenges associated with self-assessed severity labels by designing and validating features extracted from big datasets in PD, which could help identify digital biomarkers capable of providing measures of disease severity outside of a clinical environment