...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies最新文献

Freezing of gait significantly reduces the quality of life for Parkinson's disease patients by increasing the risk of injurious falls and reducing mobility. Real-time intervention mechanisms promise relief from these symptoms, but require accurate real-time, portable freezing of gait detection systems to be effective. Current real-time detection systems have unacceptable false positive freezing of gait identification rates to be adopted by the patients for real-world use. To rectify this, we propose Gait-Guard, a closed-loop, real-time, and portable freezing of gait detection and intervention system that treats symptoms in real-time with a low false positive rate. We collected 1591 freezing of gait events across 26 patients to evaluate Gait-Guard. Gait-Guard achieved a 112% reduction in the false positive intervention rate when compared with other validated real-time freezing of gait detection systems, and detected 96.5% of the true positives with an average intervention latency of just 378.5ms in a subject-independent study, making Gait-Guard a practical system for patients to use in their daily lives.

While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or simple data pre-processing can greatly limit the performance or even applicability of downstream tasks using EHRs. To address this challenge, we present HealthGAT, a novel graph attention network framework that utilizes a hierarchical approach to generate embeddings from EHR, surpassing traditional graph-based methods. Our model iteratively refines the embeddings for medical codes, resulting in improved EHR data analysis. We also introduce customized EHR-centric auxiliary pre-training tasks to leverage the rich medical knowledge embedded within the data. This approach provides a comprehensive analysis of complex medical relationships and offers significant advancement over standard data representation techniques. HealthGAT has demonstrated its effectiveness in various healthcare scenarios through comprehensive evaluations against established methodologies. Specifically, our model shows outstanding performance in node classification and downstream tasks such as predicting readmissions and diagnosis classifications.

The ability to pick up objects off the floor can degrade over time with elderly individuals, leading to a reduced quality of life and an increase in the risk of falling. Healthcare professionals have expressed an interest in monitoring the decline in pickup ability of a subject over extended periods of time and intervening when it becomes hazardous to the subject's health. The current means of evaluating pickup ability involving in-clinic patient visits is both time and financially expensive. There is a clear need for a cost-effective, remote means of pickup evaluation to ease the burden on both patients and physicians. To address these challenges, we introduce a Time-of-Pickup (ToP) solution, called ToPick, designed for the automatic assessment of pickup ability over time. The practical performance of ToPick is evident, demonstrated by a minimal median error of approximately 100 milliseconds in evaluating 20 pickup events among 10 elderly individuals. Furthermore, ToPick exhibits a high level of reliability, achieving perfect accuracy, precision, and recall scores for pickup event detection. We actualize our research findings by designing an application intended for adoption by both healthcare practitioners and elderly individuals. The app aims to reduce both time and financial costs while enabling mobile treatment for users.

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.

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.

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.

In this paper we present BayesLDM, a library for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model. BayesLDM supports modeling of Bayesian network models with a specific focus on the efficient, declarative specification of dynamic Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification with inspection of available data and outputs code for performing Bayesian inference for unknown model parameters while simultaneously handling missing data. These capabilities have the potential to significantly accelerate iterative modeling workflows in domains that involve the analysis of complex longitudinal data by abstracting away the process of producing computationally efficient probabilistic inference code. We describe the BayesLDM system components, evaluate the efficiency of representation and inference optimizations and provide an illustrative example of the application of the system to analyzing heterogeneous and partially observed mobile health data.

Sarcopenia is defined as an age-related loss of muscle mass and strength which impairs physical function leading to disability and frailty. Resistance exercises are effective treatments for sarcopenia and are critical in mitigating weight-loss induced sarcopenia in older adults attempting to lose weight. Yet, adherence to home-based regimens, which is a cornerstone to lifestyle therapies, is poor and cannot be ascertained by clinicians as no objective methods exist to determine patient compliance outside of a supervised setting. Our group developed a Bluetooth connected resistance band that tests the ability to detect exercise repetitions. We recruited 6 patients aged 65 years and older and recorded 4 specific, physical therapist-led exercises. Three blinded reviewers examined the findings and we also applied a peak Ending algorithm to the data. There were 16.6 repetitions per exercise across reviewers, with an intraclass correlation of 0.912 (95%CI: 0.853-0.953, p<0.001) between reviewers and the algorithm. Using this novel resistance band, we feasibly detected repetition of exercises in older adults.