New deep generative model improves semi-unsupervised classification.
problem Learning from datasets with sparse labels.
method Gaussian mixture deep generative model for semi-unsupervised classification.
result Superior performance on MNIST and human activity classification.
Automated GMA using accelerometers detects abnormal infant movements with human-level accuracy.
problem Undetected perinatal stroke leads to lifelong disability.
method Wearable accelerometers and Discriminative Pattern Discovery (DPD) for automated GMA.
result Automated method correctly identifies abnormal movements with human-level accuracy.
RNN model predicts handwritten characters from accelerometer and gyroscope data.
problem Online handwritten character recognition using sensor data.
method RNN-based neural network trained on gyroscope and accelerometer data.
result High accuracy on test data, achieving character prediction.
A new HAR algorithm uses U-Net for pixel-level gesture recognition.
problem Multi-class window problem in traditional HAR methods.
method U-Net network for activity labeling and prediction at each sampling point.
result Highest accuracy and F1-score compared to other methods.
The CHAMPION study clusters multi-dimensional accelerometer data to understand health links.
problem Clustering multi-dimensional data from pediatric longitudinal studies.
method Developed a finite mixture of multidimensional arrays model for clustering 4-dimensional accelerometer data.
result Demonstrated the feasibility and utility of clustering higher order data.
Deep learning models accurately recognize and estimate physical activity types and energy expenditure from wrist accelerometer data.
problem Rigorous evaluation of wrist-worn accelerometers for assessing physical activity across the lifespan.
method Built deep learning networks to extract spatial and temporal representations from time-series data, recognizing physical activity types and estimating energy expenditure.
result Deep learning models achieved high performance: F1 scores of 0.82, 0.81, and 95 for sedentary, locomotor, and lifestyle activities, respectively; root mean square error of 1.1 for EE estimation.
Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This pape…
Designs a single-sensor system for accurate lying posture detection.
problem Designing efficient in-bed lying posture tracking systems.
method Single accelerometer with machine learning algorithms (deep learning and traditional classification).
result Single accelerometer can accurately detect lying postures with high F-Score.
Study shows context-specific models improve swipe gesture authentication for smartphone users.
problem Improving swipe gesture-based continuous authentication for smartphones.
method Conducted experiments on HMOG dataset with 100 subjects, analyzing authentication error in different scenarios.
result Context-specific models are needed for different smartphone usage and human activity scenarios.
Deep learning diagnoses rotary machine faults without expert input.
problem Early detection of faults in rotary machinery to save time and money.
method Deep Convolutional Neural Network with three axis accelerometer signal input.
result High classification accuracy in fault diagnosis.
MEx dataset benchmarks HAR and multi-modal fusion for exercise quality.
problem Recognizing and evaluating exercise quality for Musculoskeletal Disorders patients.
method Multi-sensor, multi-modal dataset with four sensors (pressure mat, depth camera, accelerometers) for HAR and exercise quality assessment.
result Reference performance for each sensor identified, exposing their strengths and weaknesses.
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the varia…
Human computer interaction facilitates intelligent communication between humans and computers, in which gesture recognition plays a prominent role. This paper proposes a machine learning system to identify dynamic gestures using tri-axial acceleration data acquired from two public datasets. These datasets, uWave and So…
New gait segmentation method identifies users and adversaries with high accuracy.
problem Simultaneous identification of users and adversaries from accelerometer data.
method Geometric features and a new similarity metric for time series analysis.
result 98.79% accuracy for 6 classes (user-adversary identification) and 99.06% for binary (user only identification).
Smart watches can identify smoking gestures with high accuracy.
problem Identifying smoking gestures to prevent relapses.
method Used accelerometer sensors in smart watches and Artificial Neural Networks (ANNs).
result 85%-95% success rates in identifying smoking gestures.
Study on hyperparameter optimization for smartphone-based HAR.
problem Maintaining stable classification accuracy in HAR systems with mobile devices.
method Semi-supervised classifier and study on hyperparameter configuration.
result Adjusting hyperparameters can maintain classification accuracy.
Accelerometer measurements are the prime type of sensor information most think of when seeking to measure physical activity. On the market, there are many fitness measuring devices which aim to track calories burned and steps counted through the use of accelerometers. These measurements, though good enough for the aver…
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.
Real-time personalization for HAR models learns from new users without prior data.
problem Poor performance of HAR models on new users without labeled data.
method Incremental online domain adaptation using batch normalization.
result Personalized HAR models adapt to new users in real-time.
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.
Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of t…
Graph-based multimodal federated learning for HAR improves accuracy and privacy.
problem Challenges in HAR due to noisy data, incomplete measurements, and privacy concerns.
method Proposes GraMFedDHAR, a Graph-based Multimodal Federated Learning framework for HAR tasks, using modality-specific graphs, residual GCNs, and attention-based fusion.
result Experimental results show up to 13 percent performance improvement for MultiModalGCN under differential privacy constraints.
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.
Paper explores using hand gestures to type English letters.
problem Creating easy-to-remember, non-cumbersome gestures for typing.
method Statistical approach to handle randomness in hand movements.
result Achieved 97.33% accuracy with entire English alphabet.
Wearable smart suit tracks infant movements with high accuracy.
problem Early detection of atypical motor development in infants.
method Developed a multi-sensor smart suit for data collection, trained a deep CNN algorithm for automatic posture and movement classification.
result Setup achieves human equivalent accuracy in infant posture and movement classification.
This paper uses ML to identify prey handling in seals.
problem Automatically classify prey handling activity in seals for monitoring.
method Developed and compared three ML algorithms: Input Delay Neural Networks, Support Vector Machines, and Echo State Networks.
result Echo State Networks outperformed other algorithms in terms of accuracy and F1score.
As part of daily monitoring of human activities, wearable sensors and devices are becoming increasingly popular sources of data. With the advent of smartphones equipped with acceloremeter, gyroscope and camera; it is now possible to develop activity classification platforms everyone can use conveniently. In this paper,…
Deep learning generates efficient change-point detection methods.
problem Detecting change-points in data with various types of change and data behavior.
method Train a neural network to automatically generate detection methods.
result Neural network-based methods are competitive and outperform standard methods in various noise conditions.
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.
Pre-trained model from healthy ADLs improves gait pattern classification for Parkinson's disease.
problem Limited training data for deep learning models in healthcare applications.
method Used convolutional autoencoder to extract features from healthy ADLs data and trained a multi-layer perceptron model for Parkinson's disease classification.
result Features extracted from healthy ADLs data can be used to train an effective classification model for Parkinson's disease.
Efficient machine learning detects falls in elderly with high accuracy.
problem Rapid fall detection for elderly to prevent injuries.
method Machine learning algorithm using accelerometer data.
result 99.98% accuracy with SVM classifier on public dataset.
Automated scoring prioritizes risky driving behavior in telematic auto insurance policies.
problem Identifying risky driving behavior in telematic auto insurance policies using machine learning.
method Bayesian approach using MCMC to model propensity of policyholders to undertake trips resulting in positive classification.
result The approach improves efficiency of human resource allocation in identifying risky driving behavior.
Deep neural network improves hand gesture classification from wearable IMUs.
problem Classifying hand gestures from wearable IMUs using conventional methods.
method Deep Neural Network (DNN) with optimization objective for signal fitting.
result 3-5% improvement in classification accuracy compared to SVM and kNN.
Improved ML approach for quantifying stroke rehabilitation.
problem No tool to measure functional training after stroke.
method Refined machine learning algorithm and sensor configurations.
result Linear Discriminant Analysis (LDA) showed highest accuracy.
Deep RNN detects FoG episodes in Parkinson's disease with high accuracy.
problem Detecting freezing episodes in Parkinson's disease patients.
method Deep Recurrent Neural Network (RNN) with Long Short-Term Memory cells on 3D-accelerometer measurements.
result Frequency domain features from trunk sensor achieve an AUC score of 93% in subject-independent method.
Paper introduces a streaming compression method for monitoring pedestrian events on footbridges.
problem Storage and analysis of high-rate sensor data from instrumented infrastructure is computationally challenging.
method Develops a streaming feature-based compression method to preserve key patterns and features of pedestrian events.
result Demonstrates the trade-off between compression and accuracy during and between pedestrian events.
The behaviors of patients with depression are usually difficult to predict because the patients demonstrate the symptoms of a depressive episode without a warning at unexpected times. The goal of this research is to build algorithms that detect signals of such unusual moments so that doctors can be proactive in approac…
Technical report predicts eating and food purchasing behaviors of free-living individuals.
problem Predicting eating and food purchasing behaviors of free-living individuals.
method Applied multiple machine learning algorithms (Logistic Regression, RBF-SVM, Random Forest, Gradient Boosting) to minute-level features from sensors and environmental context.
result Gradient Boosting model had the highest mean accuracy score (0.7289) for predicting eating events before 0 to 4 minutes.
Improved anomaly detection in time series data using kervolutional neural networks.
problem Anomaly detection in time series data.
method Mixed kervolutional and convolutional layers in a temporal auto-encoder.
result The mixed model detects anomalies more sensitively in time series data.
Model creates human-like text descriptions for time series data.
problem Creating textual summaries for complex time series data that mimic human behavior.
method Utility estimation model based on Bayesian network to rank patterns in time series data.
result Output is a natural language description of time series that matches human summary.
When might human input help (or not) when assessing risk in fairness domains? Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of defendants in the ProPublica COMPAS data for risk of recidivism, and concluded that COMPAS predictions were no more accurate or fair than predictions made by human…
New methods predict walking patterns from accelerometer data.
problem Predicting individuals from walking data.
method Machine learning, inferential methods, multivariate functional regression.
result Prediction accuracy varies from 41% to 98%.
Enhances AI models with human feedback for noisy data.
problem Improving AI model alignment with human feedback in noisy environments.
method Two-stage SL+LHF framework connecting machine learning with human feedback.
result The LNCA ratio identifies conditions for SL+LHF superiority over pure SL.
New cognitive model priors improve human decision prediction.
problem Predicting human decisions with high precision remains challenging.
method Pretrained neural networks on synthetic data generated by cognitive models, fine-tuning on real human data.
result Fine-tuned neural networks achieve unprecedented state-of-the-art improvements on benchmark datasets.
Study finds resolution impacts human classification performance in MNIST data.
problem Understanding factors affecting human classification performance in machine learning.
method Empirical study of MNIST data at various resolutions.
result Derived a quantitative relationship between resolution and human classification performance.
The abstract discusses how humans use visualizations in machine learning.
problem The reliance on human involvement in AI systems and analytics.
method Review of seven steps in the ML process and different visualization techniques.
result Different visualizations are used at various stages of the ML process.
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%.
MCAL reduces labeling costs by 6x for auto-labeling data sets.
problem Expensive human annotation for ground-truth data sets.
method Iterative approach that trains a classifier to auto-label part of the data set, determining which samples to label using humans and which to label using the classifier at each step.
result 6x lower overall cost compared to human labeling the entire data set, always cheaper than competing strategies.