Cardiac interoception in the museum: A novel measure of experience.

Interoception is the perception of the body's internal signals in response to various external and internal stimuli. The present study uses a novel method adapted from the CARdiac Elevation Detection Task to examine cardiac interoception objectively and subjectively in a unique context-in the presence of art. Self-report questionnaires were used to measure subjective interoceptive awareness, subjective interoceptive accuracy, and aesthetic appreciation. For objective interoceptive accuracy and sensibility, a wearable device (Shimmer) measured heart rate (HR) and connected to a mobile application to prompt two questions: "Is your heart beating faster than usual?" and "How confident are you in your previous response?" Participants explored an art gallery for 40 minutes while the Shimmer measured their HR and randomly prompted them to answer the questions. Using a Generalized Estimating Equation model, interoceptive sensibility was not found to predict the odds of submitting a correct response. It was also found that art does not improve participants' perceptions of their HR. Finally, there was no relation between aesthetic appreciation and subjective or objective cardiac interoception. Despite lack of statistical significance, the current study's method presents an improved method by examining interoceptive accuracy in the moment under ecological conditions. To date, findings and methods used in interoception are inconsistent or flawed; the value in the current study lies in the development and demonstration of a method to examine how the environment influences the body and self-awareness across a wide variety of contexts, thereby offering a possible standardized measure of interoception for investigators to adopt.

FoG-Finder: Real-time Freezing of Gait Detection and Treatment.

Freezing of gait is a serious symptom of Parkinson's disease that increases the risk of injury through falling, and reduces quality of life. Current clinical freezing of gait treatments fail to adequately address the fall risk posed by freezing of gait symptoms, and current real-time treatment systems have high false positive rates. To address this problem, we designed a closed-loop, non-intrusive, and real-time freezing of gait detection and treatment system, FoG-Finder, that automatically detects and treats freezing of gait. To evaluate FoG-Finder, we first collected 716 freezing of gait events from 11 patients. We then compared FoG-Finder against other real-time systems with our dataset. Our system was able to achieve a 13.4% higher F1 score and a 10.7% higher overall accuracy while achieving a reduction of 85.8% in the false positive treatment rate compared with other validated real-time freezing of gait detection and treatment systems. Additionally, FoG-Finder achieved an average treatment latency of 427ms and 615ms for subject-dependent and leave-one-subject-out settings, respectively, making it a viable system to treat freezing of gait in the real-world.

Parkinson's Disease Action Tremor Detection with Supervised-Leaning Models.

People with Parkinson's Disease (PD) have multiple symptoms, such as freezing of gait (FoG), hand tremors, speech difficulties, and balance issues, in different stages of the disease. Among these symptoms, hand tremors are present across all stages of the disease. PD hand tremors have critical consequences and negatively impact the quality of PD patients' everyday lives. Researchers have proposed a variety of wearable devices to mitigate PD tremors. However, these devices require accurate tremor detection technology to work effectively while the tremor occurs. This paper introduces a PD action tremor detection method to recognize PD tremors from regular activities. We used a dataset from 30 PD patients wearing accelerometers and gyroscope sensors on their wrists. We selected time-domain and frequency-domain hand-crafted features. Also, we compared our hand-crafted features with existing CNN data-driven features, and our features have more specific boundaries in 2-D feature visualization using the t-SNE tool. We fed our features into multiple supervised machine learning models, including Logistic Regression (LR), K-Nearest Neighbours (KNNs), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs), for detecting PD action tremors. These models were evaluated with 30 PD patients' data. The performance of all models using our features has more than 90% of F1 scores in five-fold cross-validations and 88% F1 scores in the leave-one-out evaluation. Specifically, Support Vector Machines (SVMs) perform the best in five-fold cross-validation with over 92% F1 scores. SVMs also show the best performance in the leave-one-out evaluation with over 90% F1 scores.

Poster: Kinetic Tremor Measurement via IMU Sensing Data Analysis.

Tremor is a common symptom among all stages of Parkinson's Disease (PD) patients. To measure daily tremor events, we utilized IMU sensing data from wrists while PD patients were drawing. We secured 30 patients' IMU sensing data, following standard rating scale activities. With the collected data, we conducted data analysis for identifying any tremor episodes and extracting tremor amplitude. Our preliminary analysis and results show the potential of measuring kinetic tremors effectively. We plan to further analyze tremor events of PD patients via wearable sensing devices.

IMU Sensing Data-based Kinetic Tremor Detection in Parkinson's Disease Patients.

Tremor is a common symptom among Parkinson's disease (PD) patients at all stages. To measure tremor, we utilized IMU sensing data from the wrists while PD patients were drawing. With 30 patients' IMU sensing data obtained from standard tremor rating scale activities, we conducted data analysis for identifying any tremor episodes and extracting tremor amplitude. In this demo, we demonstrate that our preliminary analysis and results show the potential of measuring kinetic tremors effectively using these methods.