73 research outputs found
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
While convolutional neural networks (CNNs) have been successfully applied to
many challenging classification applications, they typically require large
datasets for training. When the availability of labeled data is limited, data
augmentation is a critical preprocessing step for CNNs. However, data
augmentation for wearable sensor data has not been deeply investigated yet.
In this paper, various data augmentation methods for wearable sensor data are
proposed. The proposed methods and CNNs are applied to the classification of
the motor state of Parkinson's Disease patients, which is challenging due to
small dataset size, noisy labels, and large intra-class variability.
Appropriate augmentation improves the classification performance from 77.54\%
to 86.88\%.Comment: ICMI2017 (oral session
Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
For people with chronic pain, the assessment of protective behavior during
physical functioning is essential to understand their subjective pain-related
experiences (e.g., fear and anxiety toward pain and injury) and how they deal
with such experiences (avoidance or reliance on specific body joints), with the
ultimate goal of guiding intervention. Advances in deep learning (DL) can
enable the development of such intervention. Using the EmoPain MoCap dataset,
we investigate how attention-based DL architectures can be used to improve the
detection of protective behavior by capturing the most informative temporal and
body configurational cues characterizing specific movements and the strategies
used to perform them. We propose an end-to-end deep learning architecture named
BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts
that are more informative to the detection of protective behavior. The approach
addresses the variety of ways people execute a movement (including healthy
people) independently of the type of movement analyzed. Through extensive
comparison experiments with other state-of-the-art machine learning techniques
used with motion capture data, we show statistically significant improvements
achieved by using these attention mechanisms. In addition, the BANet
architecture requires a much lower number of parameters than the state of the
art for comparable if not higher performances.Comment: 7 pages, 3 figures, 2 tables, code available, accepted in ACII 201
cGAN-Based High Dimensional IMU Sensor Data Generation for Therapeutic Activities
Human activity recognition is a core technology for applications such as
rehabilitation, ambient health monitoring, and human-computer interactions.
Wearable devices, particularly IMU sensors, can help us collect rich features
of human movements that can be leveraged in activity recognition. Developing a
robust classifier for activity recognition has always been of interest to
researchers. One major problem is that there is usually a deficit of training
data for some activities, making it difficult and sometimes impossible to
develop a classifier. In this work, a novel GAN network called TheraGAN was
developed to generate realistic IMU signals associated with a particular
activity. The generated signal is of a 6-channel IMU. i.e., angular velocities
and linear accelerations. Also, by introducing simple activities, which are
meaningful subparts of a complex full-length activity, the generation process
was facilitated for any activity with arbitrary length. To evaluate the
generated signals, besides perceptual similarity metrics, they were applied
along with real data to improve the accuracy of classifiers. The results show
that the maximum increase in the f1-score belongs to the LSTM classifier by a
13.27% rise when generated data were added. This shows the validity of the
generated data as well as TheraGAN as a tool to build more robust classifiers
in case of imbalanced data problem
Chronic-Pain Protective Behavior Detection with Deep Learning
In chronic pain rehabilitation, physiotherapists adapt physical activity to
patients' performance based on their expression of protective behavior,
gradually exposing them to feared but harmless and essential everyday
activities. As rehabilitation moves outside the clinic, technology should
automatically detect such behavior to provide similar support. Previous works
have shown the feasibility of automatic protective behavior detection (PBD)
within a specific activity. In this paper, we investigate the use of deep
learning for PBD across activity types, using wearable motion capture and
surface electromyography data collected from healthy participants and people
with chronic pain. We approach the problem by continuously detecting protective
behavior within an activity rather than estimating its overall presence. The
best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross
validation. When protective behavior is modelled per activity type, performance
is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for
sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This
performance reaches excellent level of agreement with the average experts'
rating performance suggesting potential for personalized chronic pain
management at home. We analyze various parameters characterizing our approach
to understand how the results could generalize to other PBD datasets and
different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on
Computing for Healthcar
Balance Measures Derived from Insole Sensor Differentiate Prodromal Dementia with Lewy Bodies
Dementia with Lewy bodies is the second most common type of neurodegenerative
dementia, and identification at the prodromal stagei.e., mild cognitive
impairment due to Lewy bodies (MCI-LB)is important for providing appropriate
care. However, MCI-LB is often underrecognized because of its diversity in
clinical manifestations and similarities with other conditions such as mild
cognitive impairment due to Alzheimer's disease (MCI-AD). In this study, we
propose a machine learning-based automatic pipeline that helps identify MCI-LB
by exploiting balance measures acquired with an insole sensor during a 30-s
standing task. An experiment with 98 participants (14 MCI-LB, 38 MCI-AD, 46
cognitively normal) showed that the resultant models could discriminate MCI-LB
from the other groups with up to 78.0% accuracy (AUC: 0.681), which was 6.8%
better than the accuracy of a reference model based on demographic and clinical
neuropsychological measures. Our findings may open up a new approach for timely
identification of MCI-LB, enabling better care for patients
Automated Intelligent Cueing Device to Improve Ambient Gait Behaviors for Patients with Parkinson\u27s Disease
Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods to analyze gait signals collected through wearable sensors and accurately identify FoG episodes. We also investigated the potential of predicting the symptoms before their actual occurrence.
We collected data from seven participants with PD using two Inertial Measurement Units (IMUs) on ankles. In our first study, we extracted engineered features from the signals and used machine learning (ML) methods to identify FoG episodes. We tested the performance of models using patient-dependent and patient-independent paradigms. The former models achieved 92.5% and 89.0% for average sensitivity and specificity, respectively. However, the conventional binary classification methods fail to accurately classify data if only data from normal gait periods are available. In order to identify FoG episodes in participants who did not freeze during data collection sessions, we developed a Deep Gait Anomaly Detector (DGAD) to identify anomalies (i.e., FoG) in the signals. DGAD was formed of convolutional layers and trained to automatically learn features from signals. The convolutional layers are followed by fully connected layers to reduce the dimensions of the features. A k-nearest neighbors (kNN) classifier is then used to classify the data as normal or FoG. The models identified 87.4% of FoG onsets, with 21.9% being predicted on average for each participant. This study demonstrates our algorithm\u27s potential for delivery of preventive cues. The DGAD algorithm was then implemented in an Android application to monitor gait patterns of PD patients in ambient environments. The phone triggered vibrotactile and auditory cues on a connected smartwatch if an FoG episode was identified. A 6-week in-home study showed the potentials for effective treatment of FoG severity in ambient environments using intelligent cueing devices
Pragmatic Evaluation of Health Monitoring & Analysis Models from an Empirical Perspective
Implementing and deploying several linked modules that can conduct real-time analysis and recommendation of patient datasets is necessary for designing health monitoring and analysis models. These databases include, but are not limited to, blood test results, computer tomography (CT) scans, MRI scans, PET scans, and other imaging tests. A combination of signal processing and image processing methods are used to process them. These methods include data collection, pre-processing, feature extraction and selection, classification, and context-specific post-processing. Researchers have put forward a variety of machine learning (ML) and deep learning (DL) techniques to carry out these tasks, which help with the high-accuracy categorization of these datasets. However, the internal operational features and the quantitative and qualitative performance indicators of each of these models differ. These models also demonstrate various functional subtleties, contextual benefits, application-specific constraints, and deployment-specific future research directions. It is difficult for researchers to pinpoint models that perform well for their application-specific use cases because of the vast range of performance. In order to reduce this uncertainty, this paper discusses a review of several Health Monitoring & Analysis Models in terms of their internal operational features & performance measurements. Readers will be able to recognise models that are appropriate for their application-specific use cases based on this discussion. When compared to other models, it was shown that Convolutional Neural Networks (CNNs), Masked Region CNN (MRCNN), Recurrent NN (RNN), Q-Learning, and Reinforcement learning models had greater analytical performance. They are hence suitable for clinical use cases. These models' worse scaling performance is a result of their increased complexity and higher implementation costs. This paper compares evaluated models in terms of accuracy, computational latency, deployment complexity, scalability, and deployment cost metrics to analyse such scenarios. This comparison will help users choose the best models for their performance-specific use cases. In this article, a new Health Monitoring Metric (HMM), which integrates many performance indicators to identify the best-performing models under various real-time patient settings, is reviewed to make the process of model selection even easier for real-time scenarios
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