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Found 13 papers, 2 papers with code
Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network
We use very low sampling rates for both signal types to reduce battery consumption.
Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions
Yet for large scale studies, obtaining even the necessary coarse ground-truth is not trivial, as a complete neurological evaluation is required.
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds
The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome.
Chewing Detection from Commercial Smart-glasses
Automatic dietary monitoring has progressed significantly during the last years, offering a variety of solutions, both in terms of sensors and algorithms as well as in terms of what aspect or parameters of eating behavior are measured and monitored.
A Bottom-up method Towards the Automatic and Objective Monitoring of Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors
In particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of 0. 863 for the detection of puffs and an F1-score/Jaccard index equal to 0. 878/0. 604 towards the temporal localization of smoking sessions during the day.
Bite-Weight Estimation Using Commercial Ear Buds
While automatic tracking and measuring of our physical activity is a well established domain, not only in research but also in commercial products and every-day life-style, automatic measurement of eating behavior is significantly more limited.
Self-Supervised Feature Learning of 1D Convolutional Neural Networks with Contrastive Loss for Eating Detection Using an In-Ear Microphone
A common bottleneck for developing and training machine learning algorithms is obtaining labeled data for training supervised algorithms, and in particular ground truth annotations.
Recognition of food-texture attributes using an in-ear microphone
Our approach performs very well in recognizing crispiness (0. 95 weighted accuracy on new subjects and 0. 93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness.
An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images
Diabetic Retinopathy (DR) is a leading cause of vision loss globally.
A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches
The increased worldwide prevalence of obesity has sparked the interest of the scientific community towards tools that objectively and automatically monitor eating behavior.
Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses.
Span error bound for weighted SVM with applications in hyperparameter selection
Experiments on 14 benchmark data sets and data sets with importance scores for the training instances show that: (a) the condition for the existence of span in weighted SVM is satisfied almost always; (b) the span-rule is the most effective method for weighted SVM hyperparameter selection; (c) the span-rule is the best predictor of the test error in the mean square error sense; and (d) the span-rule is efficient and, for certain problems, it can be calculated faster than $K$-fold cross-validation.
Learning Local Feature Aggregation Functions with Backpropagation
This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem).
