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Found 23 papers, 6 papers with code
MicroT: Low-Energy and Adaptive Models for MCUs
On MCUs, compared to the standard full model inference, MicroT can save up to about 29. 13% in energy consumption.
Interpreting Differentiable Latent States for Healthcare Time-series Data
Understanding the meaning of latent states is crucial for interpreting machine learning models, assuming they capture underlying patterns.
A Markov Chain Model for Identifying Changes in Daily Activity Patterns of People Living with Dementia
Using the COVID-19 pandemic as a natural experiment, we conducted linear mixed-effects modelling to examine changes in mean kitchen activity within a subset of 21 households of PLWD that were continuously monitored for 499 days.
Information Theory Inspired Pattern Analysis for Time-series Data
For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains.
Loss Adapted Plasticity in Deep Neural Networks to Learn from Data with Unreliable Sources
When data is streaming from multiple sources, conventional training methods update model weights often assuming the same level of reliability for each source; that is: a model does not consider data quality of each source during training.
Using Entropy Measures for Monitoring the Evolution of Activity Patterns
The study uses Internet of Things (IoT) enabled solutions for continuous monitoring of in-home activity, sleep, and physiology to develop care and early intervention solutions to support people living with dementia (PLWD) in their own homes.
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals.
Multimodal Federated Learning on IoT Data
Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications.
Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia
We train and test the proposed model on a dataset from a clinical study.
An Intelligent Bed Sensor System for Non-Contact Respiratory Rate Monitoring
We present an IoT-based intelligent bed sensor system that collects and analyses respiration-associated signals for unobtrusive monitoring in the home, hospitals and care units.
A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare Data
A Bayesian approach to impute missing values and creating augmented samples in high dimensional healthcare data is proposed in this work.
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia
We have developed an integrated platform to collect in-home sensor data and performed an observational study to apply machine learning models for agitation and UTI risk analysis.
Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots
The hybrid-face robot concept has been modified, implemented, and released in the commercial IoT robotic platform Miko (My Companion), an affective robot with facial and conversational features currently in use for human-robot interaction in children by Emotix Inc. We demonstrate that human EEG responses to Miko emotions are comparative to neurophysiological responses for actual human facial recognition.
Electroencephalogram (EEG)
Robotics
Deep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in long-term conditions such as dementia.
Semi-supervised Federated Learning for Activity Recognition
In this paper, we propose an activity recognition system that uses semi-supervised federated learning, wherein clients conduct unsupervised learning on autoencoders with unlabelled local data to learn general representations, and a cloud server conducts supervised learning on an activity classifier with labelled data.
Verifying the Causes of Adversarial Examples
The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence.
Continual Learning Using Multi-view Task Conditional Neural Networks
The changes in goals or data are referred to as new tasks in a continual learning model.
Continual Learning Using Bayesian Neural Networks
The catastrophic forgetting is an inevitable problem in continual learning models for dynamic environments.
Continual Learning in Deep Neural Network by Using a Kalman Optimiser
The results show that our approach enables the model to continually learn and adapt to the new changes without forgetting the previously learned tasks.
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment.
Machine learning for Internet of Things data analysis: A survey
This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case.
Segment Parameter Labelling in MCMC Mean-Shift Change Detection
This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models.
A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams
Cities have been a thriving place for citizens over the centuries due to their complex infrastructure.
