Detecting early-stage Parkinson's disease from gait data.

Parkinson's disease is a chronic and progressive neurodegenerative disorder with an estimated 10 million people worldwide living with PD. Since early signs are benign, many patients go undiagnosed until the symptoms get severe and the treatment becomes more difficult. The symptoms start intermittently and gradually become continuous as the disease progresses. In order to detect and classify these minute differences between gaits in early PD patients, we propose to use dynamic time warping (DTW). For a given set of gait data from a patient, the DTW algorithm computes the difference between any two gait cycles in the form of a warping path, which reveals small time differences between gait cycles. Once the time-warping information between all possible pairs of gait cycles is used as the main source of gait features, K-means clustering is used to extract the final features. These final features are fed to a simple logistic regression to easily and successfully detect early PD symptoms, which was reported as challenging using conventional statistical features. In addition, the use of DTW ensures that the obtained results are not affected by the differences in the style and speed of walking of a subject. Our approach is validated for the gait data from 83 subjects at early stages of PD, 10 subjects at moderate stages of PD, and 73 controls using the Leave-One-Out and N-fold cross-validation techniques, with a detection accuracy of over 98%. The high classification accuracy validated from a large data set suggests that these new features from DTW can be effectively used to help clinicians diagnose the disease at the earliest. Even though PD is not completely curable, early diagnosis would help clinicians to start the treatment from the beginning thereby reducing the intensity of symptoms at later stages.

Predicting Early Stage Drug Induced Parkinsonism using Unsupervised and Supervised Machine Learning.

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variations in gait accelerometer data due to the intermittency of tremor at the initial stages is examined. These variations are used to train a logistic regression model to predict subjects with early-stage DIP. The logistic classifier predicts if a subject is a DIP or control with approximately 89% sensitivity and 96% specificity. This paper discusses the algorithm used to extract the features in gait data for training the classifier to predict DIP at the earliest.Clinical Relevance- Diagnosing the disease and the causative drug is vital as the physical health of a patient who is mentally unstable can deteriorate with prolonged usage of the drug. The proposed model helps clinicians to diagnose the disease at the onset of tremors with an accuracy of 93.58%.

A lobster-inspired bending module for compliant robotic applications.

Ideally, robots may be designed to adapt to different tasks such as heavy lifting and handling delicate objects, in which the requirements in force compliance and position accuracy vary dramatically. While conventional rigid actuators are usually characterized by high precision and large force output, soft actuators are designed to be more compliant and flexible. In this paper, a lobster-inspired bending module with compliant actuation, enhanced torque output, and reconfigurability in assembling is presented. It is also capable of accurate control of its angular position with variable stiffness. Inspired by the anatomic structure of the lobster leg joint, the bending module has antagonistic soft chambers for actuation and rigid shells for structural protection and support. Theoretical models have been developed and their capability of independently adjusting both the bending angle and stiffness has been evaluated through experiments. A control strategy is constructed to realize angle control and stiffness adaptation. In order to demonstrate various applications of the proposed bending module, reconfigurable robotic fingers are assembled and shown to be capable of generating different motion profiles. In addition, robotic grippers are built for lifting both delicate and heavy objects, demonstrating applications that require both high force and compliant handling.

Using accelerometer as a diagnostic tool to detect drug-induced parkinsonism (DIP) secondary to first-generation anti-psychotic medications.

OBJECTIVE: The objective of this study is to examine the effectiveness of an accelerometer-based compact system in detecting and quantifying drug-induced parkinsonism (DIP) in patients with schizophrenia. METHOD: A pilot study controlled clinical trial comprising 6 people with schizophrenia and 11 control subjects was conducted at Alfred Health, Melbourne. Participants had their movements assessed using Barnes Akathisia Rating Scale (BARS), Simpson Angus Scale (SAS) and Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) followed by an assessment of gait using three triaxial accelerometers. RESULTS: Median BARS, SAS, MDS-UPDRS III and accelerometer scores were significantly higher for patients with schizophrenia than controls. Accelerometers detected three times more rest tremor than clinical rating scales. Patients with schizophrenia had 70% of their dynamic acceleration at frequencies between 4 and 10 Hz, which is almost twice that observed in the control population (38%). Accelerometer scores were significantly correlated with BARS scores. CONCLUSION: Accelerometers were able to accurately detect patients with DIP better than some clinical rating scale including the SAS. Further larger-scale studies must be conducted to further demonstrate the accuracy of accelerometers in detecting DIP.

An alignment maximization method for the kinematics of the eye and eye-head fixations.

The orientation of human eyes is uniquely defined with respect to their gaze direction, known as Donders' law. Further, the manner in which the eyes follow Donders' law varies as a function of the situation. When the head is stationary, the Donders' surfaces are flat planes but they tilt when eye fixation distance changes. These planes also shift and rotate when head orientation changes with respect to the direction of gravito-inertial acceleration. When the head is free to rotate, the Donders' surfaces are twisted. In this paper, we present a systematic method to analyze the kinematics of the eye under different gaze situations utilizing the measurement of alignment between various coordinate frames. Kinematic equations are presented for various eye movements ranging from simple head-fixed monocular shifts of eye gaze to complex eye-head shifts of gaze. At each stage, we show that simulated eye orientations that derived from our equations are able to capture the variations of Donders' surfaces and they are comparable with experimental results in the literature. The final equations we propose provide the unified kinematics of head-upright far gaze, head-upright binocular fixation, head static tilted monocular gaze and head-free monocular gaze.