Athanasios Tsanas

Kieran Woodward, Eiman Kanjo, Athanasios Tsanas

The quantification of emotional states is an important step to understanding wellbeing. Time series data from multiple modalities such as physiological and motion sensor data have proven to be integral for measuring and quantifying emotions. Monitoring emotional trajectories over long periods of time inherits some critical limitations in relation to the size of the training data. This shortcoming may hinder the development of reliable and accurate machine learning models. To address this problem, this paper proposes a framework to tackle the limitation in performing emotional state recognition on multiple multimodal datasets: 1) encoding multivariate time series data into coloured images; 2) leveraging pre-trained object recognition models to apply a Transfer Learning (TL) approach using the images from step 1; 3) utilising a 1D Convolutional Neural Network (CNN) to perform emotion classification from physiological data; 4) concatenating the pre-trained TL model with the 1D CNN. Furthermore, the possibility of performing TL to infer stress from physiological data is explored by initially training a 1D CNN using a large physical activity dataset and then applying the learned knowledge to the target dataset. We demonstrate that model performance when inferring real-world wellbeing rated on a 5-point Likert scale can be enhanced using our framework, resulting in up to 98.5% accuracy, outperforming a conventional CNN by 4.5%. Subject-independent models using the same approach resulted in an average of 72.3% accuracy (SD 0.038). The proposed CNN-TL-based methodology may overcome problems with small training datasets, thus improving on the performance of conventional deep learning methods.

Ben Nicholls, Chee Siang Ang, Eiman Kanjo et al.

Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38)=58.243, p<0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes.

Siddharth Arora, Ladan Baghai-Ravary, Athanasios Tsanas

Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.