DeepBeat uses deep learning to assess signal quality and detect arrhythmia in wearable devices.
problem Detecting atrial fibrillation from wearable devices with noise.
method Multi-task deep learning approach using convolutional denoising autoencoders.
result Significantly improved AF detection accuracy compared to traditional methods.
Extracts behavioral features from smartphone and wearable data.
problem Processing raw data streams from smartphones and wearables for human behavior analysis.
method Generic framework for processing raw data streams and extracting behavioral features.
result Extracts useful features related to non-verbal human behavior from raw data streams.
Wrist movements can reveal digits, posing security risks.
problem Security vulnerabilities in wrist wearable devices.
method Machine learning model trained on wrist movement data.
result 100% accuracy in predicting digits via wrist movement.
Research uses AI and RNN for detecting falls in wearable devices.
problem Detecting falls for timely assistance to prevent injuries.
method Recurrent Neural Network (RNN) with LSTM blocks for online fall detection.
result The RNN-based classifier outperformed the SisFall authors' results.
Deep learning detects tennis players' flow state from wearable data.
problem Lack of psychological feedback in wearable devices for sports performance.
method Trained deep neural networks on wearable data and coach labels.
result Deep neural networks achieved 98% accuracy in detecting flow state.
Improved cardiac arrhythmia detection in wearable devices with neural networks.
problem Resource constraints in low-power wearable devices for accurate arrhythmia detection.
method Adapted a convolutional-recurrent neural network to a low-power microcontroller, optimizing for precision and memory usage.
result Reduced F1 score from 0.8 to 0.784 in fixed-point precision, with a 195.6KB memory footprint and 33.98MOps/s throughput. New RL method optimizes power and accuracy for activity recognition.
problem Balancing power consumption and accuracy in wearable devices for activity recognition.
method Reinforcement Learning with multiple feedback sources for feature selection.
result Achieved good trade-off between power consumption and accuracy.
New system uses wearable bio-signals for easy authentication.
problem Security of private information on wearables is a concern.
method Context-dependent soft-biometric authentication using heart rate, gait, and breathing audio.
result Binary SVM with RBF kernel achieves high accuracy and low EER.
Study predicts diabetes biomarkers using wearable data.
problem Understanding and predicting diabetes progression.
method Wide and deep neural network with LSTM structure.
result Model predicts biomarkers with low error rates.
A wearable device-based sleep stage classifier using feature learning and RNNs.
problem Automatic sleep stage classification using wearable devices.
method Multi-level feature learning framework and RNN classifier with BLSTM.
result The algorithm achieves high precision, recall, and F1 scores in both resting and comprehensive groups.
Novel CNN array for sign language recognition using wearable IMUs.
problem Efficiently recognizing sign language from wearable IMU signals.
method Two-dimensional Convolutional Neural Network array architecture for Indian sign language recognition.
result Peak classification accuracies of 94.20% for general sentences and 95.00% for interrogative sentences achieved.
Paper presents a stress prediction model for students using wearable data.
problem Predicting students' stress levels from wearable data is challenging.
method Used Auto-encoders and Multitask learning to predict stress from sensor data and covariates.
result Model improved stress prediction by 45.6% on StudentLife dataset.
Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
problem Improving seizure detection accuracy for wearable devices.
method Transfer learning with tensor kernel machine using canonical polyadic decomposition.
result Patient fine-tuned model achieves high performance with smaller model size.
Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have so…
MoCA uses a novel autoencoder to analyze multi-modal health data.
problem Challenges in analyzing continuous multi-modal health data from wearable devices.
method Proposes MoCA, a self-supervised learning framework combining transformer and masked autoencoder methods.
result Demonstrates strong performance boosts across reconstruction and classification tasks.
Develops a method to denoise and analyze wearable ECGs.
problem Noisy ECGs from wearable devices.
method Statistical model, beat-to-beat representation, factor analysis.
result Upper bound on performance quantified and compared.
PPGnet model estimates heart rate from PPG signals without motion artifacts.
problem Wearable PPG devices struggle with motion artifacts.
method End-to-end deep learning model using 8-second PPG signals.
result Achieved mean absolute error of 3.36+-4.1 BPM on IEEE SPC 2015 dataset.
Bayesian active learning improves stress and affect detection on wearable devices.
problem Handling unlabeled data in real-time for stress and affect detection.
method Bayesian Neural Networks with Monte-Carlo Dropout and suitable acquisition functions.
result Framework achieves significant efficiency boost and low number of acquired pool points.
LEAF benchmarks federated learning challenges.
problem Federated learning challenges with scale and heterogeneity.
method Modular benchmarking framework with open-source datasets and implementations.
result Captures obstacles and intricacies of practical federated environments.
Reservoir computing models classify ECG signals for patient-adaptive monitoring.
problem Classifying ECG signals for patients with imbalanced heartbeat classes.
method Reservoir computing paradigm applied to recurrent neural networks (RNNs).
result Accurate patient-adaptive ECG classifier that handles imbalanced classes.
Deep learning detects Parkinson's disease severity from wearable data.
problem Measuring Parkinson's disease severity from accessible biomarkers.
method Developed and evaluated deep learning models on sensor data from wearable devices.
result Deep learning models outperform classical machine learning models in classifying Parkinson's disease severity.
Deep CNN-RNN model classifies breathing sounds for respiratory disease diagnosis.
problem Automated diagnosis of respiratory diseases using wearable devices.
method Patient-specific model tuning and local log quantization of weights.
result 71.81% accuracy on leave-one-out validation with patient-specific data.
A wrist-worn device system for user authentication using writing behavior analysis.
problem User authentication through writing behavior for wearable devices.
method Dynamic Time Wrapping and Savitzky-Golay filter for fine-grained writing metrics.
result The proposed system achieves high accuracy in user identification with low false-positive and false-negative rates.
Bayesian method for imputing actigraph data from mobile devices.
problem Imputing missing actigraph data from mobile devices.
method Bayesian inference and hierarchical dynamic linear model.
result Statistical learning of time-varying impact of explanatory variables on acceleration.
Proposes privacy-preserving sensor data transformations to prevent user re-identification and sensitive activity inference.
problem Privacy threats from shared sensor data and potential user re-identification.
method Mechanisms to transform sensor data to eliminate patterns for re-identification and sensitive activity inference, while maintaining minor utility loss.
result Reduced user re-identification accuracy to random guess level and prevented inference of sensitive activities.
New eGRU unit improves keyword spotting on ultra-low-power devices.
problem Resource constraints of edge devices for neural network deployment.
method Optimized recurrent unit architecture for ultra-low power.
result eGRU is 60x faster and 10x smaller than GRU, maintaining accuracy.
Activity2vec learns representations from wearable activity data.
problem Proactive screening and monitoring of chronic conditions.
method Adversarial unsupervised representation learning with three components.
result Activity2vec outperforms many baselines in disorder prediction tasks.
Deep learning classifies animal behavior from wearable accelerometers.
problem Classifying animal behavior from accelerometer data.
method End-to-end deep neural network with IIR and FIR filters.
result Outperforms state-of-the-art algorithms in real-time classification.
Paper develops methods for evaluating mHealth interventions using historical data.
problem Evaluating the long-term effectiveness of mHealth interventions designed for near-term outcomes.
method Develops off-policy estimation techniques to infer long-term average outcomes from historical data.
result Provides estimators and confidence intervals for evaluating mHealth policies.
Deep learning detects atrial fibrillation from wearable sensor data.
problem Detecting atrial fibrillation from raw sensor data.
method Convolutional-recurrent neural network with long short-term memory, end-to-end learning.
result State-of-the-art AFib detection with high accuracy.
Anonymizes sensor data to protect user privacy.
problem Protecting user privacy from motion sensor data.
method Information-theoretic approach and multi-objective loss function for deep autoencoders.
result Anonymized sensor data preserves activity recognition accuracy above 92% while keeping user identification accuracy below 7%.
This research synthesizes 12-lead ECG from a single-lead ECG device.
problem Limited cardiac diagnostics from single-lead ECG devices.
method Random forest machine learning model using historical 12-lead recordings.
result Synthesized 12-lead ECG with accuracies exceeding 90%.
Smartwatch HRV measurements improved with machine learning.
problem Systematic error in HRV measurements from consumer smartwatches.
method Explanatory and predictive modeling using accelerometer data.
result Error in HRV measurements can be minimized by machine learning.
PhysioMTL learns personalized HRV rhythms from wearable data, improving prediction and counterfactual analysis.
problem Challenges in interpreting HRV measurements due to variability and stressors.
method PhysioMTL combines Optimal Transport and MTL to learn personalized diurnal rhythms from heterogeneous data.
result PhysioMTL outperforms other methods in predicting unseen subjects and generating counterfactual effects.
Tiny Eats GRU detects eating episodes on a microcontroller.
problem Automatic dietary monitoring on low-power devices.
method Shallow gated recurrent unit (GRU) architecture on Arm Cortex M0+.
result Tiny Eats GRU achieves 95.15% accuracy with 4% memory usage and 6 ms latency.
Deep learning detects arrhythmia from RR-interval ECG data.
problem Diagnosing arrhythmia using ECG data.
method Convolutional neural network (CNN) on time-sliced RR-interval data.
result Compact system achieves accurate arrhythmia detection.
Study proposes BFEL framework for privacy-preserving FL in personalized healthcare.
problem Privacy and security concerns in traditional cloud-centric ML, especially in wearable devices.
method Develops a blockchain-enhanced federated edge learning (BFEL) framework based on FedCurv, incorporating fisher information matrix and public key encryption.
result Significant reduction in communication cost and high efficiency for federated training on non-iid and heterogeneous data.
Two approaches scale up DNN optimization for diverse edge devices.
problem Optimizing DNNs for edge devices with varying performance requirements.
method Reuse performance predictors on proxy devices and build scalable predictors.
result Optimized DNN designs for many different edge devices without lengthy optimization.
A wearable EEG headband detects primary colors from brain activity for color perception.
problem Detecting primary colors from brain activity for color perception.
method Spectral power features, statistical features, and correlation features from continuous Morlet wavelet transform; dimensionality reduction techniques like Forward Feature Selection and Stacked Autoencoders; Random Forest Classifier.
result Best overall accuracy of 80.6% for intra-subject classification.
Deep neural network detects falls from IoT sensor data.
problem Detecting irregular patterns in IoT streaming data for fall detection.
method Deep neural network model using accelerometer data.
result 98.75 percent accuracy in detecting falls.
A fall is an abnormal activity that occurs rarely, so it is hard to collect real data for falls. It is, therefore, difficult to use supervised learning methods to automatically detect falls. Another challenge in using machine learning methods to automatically detect falls is the choice of engineered features. In this p…
ESPRESSO segments time-series data for better human activity recognition.
problem Segmenting high-dimensional time-series data for applications like HAR.
method ESPRESSO combines entropy and shape analysis for multi-dimensional time-series segmentation.
result ESPRESSO outperforms four state-of-the-art methods across seven datasets.
Study detects and mitigates stealthy DDoS attacks in IoT networks.
problem Stealthy DDoS attacks in IoT networks.
method Anomaly-based IDS for timely detection and mitigation.
result Demonstrated capability of detecting and mitigating small attack size per source.
TR-Nets compress deep networks by 11x for LeNet-5 and 243x for Wide ResNet.
problem Large neural networks require excessive memory and computation.
method Tensor Ring factorization to compress fully connected and convolutional layers.
result TR-Nets can compress LeNet-5 by 11x and Wide ResNet by 243x with minimal accuracy loss.
Paper identifies resting positions using EGG, ECG, respiration rate, and SpO2.
problem Identifying the resting position for health monitoring.
method Hybrid stacked ensemble machine learning model combining Decision tree, Random Forest, and Xgboost.
result 100% accurate prediction of resting positions.
In this work we investigate intra-day patterns of activity on a population of 7,261 users of mobile health wearable devices and apps. We show that: (1) using intra-day step and sleep data recorded from passive trackers significantly improves classification performance on self-reported chronic conditions related to ment…
EasyTL simplifies transfer learning by leveraging intra-domain structures.
problem Transfer learning difficulty and constraints in limited labeled data.
method Exploits intra-domain structures to avoid model selection and hyperparameter tuning.
result Achieves competitive performance with reduced complexity and computational efficiency.
RAN model recognizes multiple activities from unlabeled sensor data.
problem Handling weakly labeled multi-activity data from wearable sensors.
method Recurrent Attention Networks (RAN) for sequential multi-activity recognition and localization.
result RAN model can infer multiple activities and determine activity locations from unlabeled data.