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While Parkinson’s disease (PD) is typically characterized by motor disorder, there is also evidence of diminished emotion perception in PD patients. This study examines the utility of electroencephalography (EEG) signals to understand emotional differences between PD and healthy controls (HCs), and for automated PD detection. Employing traditional machine learning and deep learning methods on multiple EEG descriptors, we explore (a) dimensional and categorical emotion recognition and (b) PD versus HC classification from multiple descriptors characterizing emotional EEG signals. Our results reveal that PD patients comprehend arousal better than valence and, among emotion categories, fear, disgust, and surprise less accurately, and sadness most accurately. Mislabeling analyses confirm confounds among opposite-valence emotions for PD data. Emotional EEG responses also achieve near-perfect PD versus HC recognition. Cumulatively, our study demonstrates that (a) examining implicit responses alone enables (i) discovery of valence-related impairments in PD patients and (ii) differentiation of PD from HC and that (b) emotional EEG analysis is an ecologically valid, effective, practical, and sustainable tool for PD diagnosis vis-à-vis self-reports, expert assessments, and resting-state analysis.

Abstract Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.

BackgroundTo better understand the biomechanical profile of direct head impacts and the game scenarios in which they occur in Rugby Union, there is a need for an on-field validation of a new instrumented mouthguard (IMG) against the reference standard. This study considers the potential of a combined biomechanical (IMG) and video analysis approach to direct head impact recognition, both of which in isolation have limitations. The aim of this study is to assess the relationship between an instrumented mouthguard and video analysis in detection of direct head impacts in rugby union.DesignPilot Study - Observational Cohort designMethodsThe instrumented mouthguard was worn by ten (3 backs, 7 forwards) professional Rugby Union players during the 2020–21 Gallagher Premiership (UK) season. Game-day video was synchronized with timestamped head acceleration events captured from the instrumented mouthguard. Direct Head Impacts were recorded in a 2 × 2 contingency table to determine sensitivity. Impact characteristics were also collected for all verified head impacts to further the understanding of head biomechanics during the game.ResultsThere were 2018 contact events that were reviewed using video analysis. Of those 655 were categorized as direct head impacts which also correlated with a head acceleration event captured by the IMG. Sensitivity analysis showed an overall sensitivity of 93.6% and a positive predictive value (PPV of 92.4%). When false positives were excluded due to ball out of play, mouthguard removal or handling after a scoring situation or stoppage, PPV was improved (98.3%). Most verified head impacts occurred in and around the ruck contest (31.2%) followed by impacts to the primary tackler (28.4%).ConclusionThis pilot validation study demonstrates that this IMG provides a highly accurate measurement device that could be used to complement video verification in the recognition of on-field direct head impacts. The frequency and magnitude of direct head impacts derived from specific game scenarios has been described and allows for greater recognition of high-risk situations. Further studies with larger sample sizes and in different populations of Rugby Union players are required to develop our understanding of head impact and enable strategies for injury mitigation.

Functional near-infrared spectroscopy (fNIRS) can be used to study activity patterns in different brain cortical areas involved in balance control. This systematic review aims to report on studies in which balance performance has been quantitatively assessed concurrent with fNIRS neuroimaging. Following the PRISMA guidelines, relevant keywords were used for the search through the Scopus and Web of Science databases. Sixty-eight studies met the inclusion criteria and were imported for data extraction. Information on balance assessment protocols, alterations to the balance control loop, brain regions of interest, fNIRS parameters, the relationships found between brain activity and balance performance, and participant cohort types was extracted. The common balance tasks in fNIRS studies were standing and walking. Standing balance control was mainly measured through sway parameters using force platforms. Walking performance was evaluated through gait parameters mostly assessed by floor sensors or inertial sensors. Some of the balance tasks were challenged through sensory manipulation or dual task interference. Brain activity monitoring via fNIRS was mainly utilized to measure oxygenated haemoglobin concentration in frontal cortex. Out of the 68 included articles, 22 studies investigated and found the relationships between activity patterns in different cortical areas and balance measures. In 32 studies, the effects of different factors such as long-term, biological, and psychological conditions on brain activity and balance performance were studied. This study provides a systematic review on fNIRS studies in which quantitative balance assessment is employed to provide a better understanding of neuromotor control of balance.

Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain’s active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin (ΔHBO2) and deoxygenated haemoglobin (ΔHHB). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both ΔHBO2 and ΔHHB measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of 68.51%±9.02% in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of ΔHBO2 and ΔHHB. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS.

Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients’ self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions, 93.2±8% in identification of pain, 68.9±10% in the multiclass problem, and 56.0±8% for the identification of pain location. These results identify EDA as the superior sensor in our experimental conditions. Future work is required to validate the obtained features to improve its feasibility in more realistic scenarios. Finally, this study proposes EDA as a candidate to design a tool that can assist clinicians in the assessment of acute pain of nonverbal patients.

This paper presents a novel adversarial deep neural network to estimate human poses from still images, such as those obtained from CCTV and the Internet-of-Things (IoT) devices. Specifically, the proposed adversarial deep neural network exhibits the spatial hierarchy of human body parts considering the fact that predicting the position of some parts is more challenging than others. The generative and the discriminative portions of the proposed adversarial deep neural network are designed to encode the spatial relationship between the parts in the first stage of the hierarchy (parents) and the parts in the second stage of the hierarchy (children). Each of the generator and the discriminator networks is designed as two components, which are sequentially connected together to infer rich appearance potentials and to encode not only the likelihood of the part's existence but also the relationships between each body part and its parent. The method is evaluated on three different datasets, whose findings suggest that the proposed network achieves comparable results with other competing state-of-the-art approaches.

Automatic analysis of human behaviour in large collections of videos is rapidly gaining interest, even more so with the advent of file sharing sites such as YouTube. From one perspective, it can be observed that the size of feature vectors used for human action recognition from videos has been increasing enormously in the last five years, in the order of ∼100–500K. One possible reason might be the growing number of action classes/videos and hence the requirement of discriminating features (that usually end up to be higher‐dimensional for larger databases). In this study, the authors review and investigate feature projection as a means to reduce the dimensions of the high‐dimensional feature vectors and show their effectiveness in terms of performance. They hypothesise that dimensionality reduction techniques often unearth latent structures in the feature space and are effective in applications such as the fusion of high‐dimensional features of different types; and action recognition in untrimmed videos. They conduct all the authors’ experiments using a Bag‐of‐Words framework for consistency and results are presented on large class benchmark databases such as the HMDB51 and UCF101 datasets.

With the advancement of technology in both hardware and software, estimating human affective states has become possible. Currently, movie clips are used as they are a widely-accepted method of eliciting emotions in a replicable way. However, cultural differences might influence the effectiveness of some video clips to elicit the target emotions. In this paper, we describe several sensors and techniques to measure, validate and investigate the relationship between cultural acceptance and eliciting universal expressions of affect using movie clips. For emotion elicitation, a standardised list of English language clips, as well as an initial set of Arabic video clips are used for comparison. For validation, bio-signal devices to measure physiological and behavioural responses associated with emotional stimuli are used. Physiological and behavioural responses are measured from 29 subjects of Arabic background while watching the selected clips. For the six emotions’ classification, a multiclass SVM (six-class) classifier using the physiological and behavioural measures as input results in a higher recognition rate for elicited emotions from Arabic video clips (avg. 60%) compared to the English video clips (avg. 52%). These results might reflect that using video clips from the subjects’ culture is more likely to elicit the target emotions. Besides measuring the physiological and behavioural responses, an online survey was carried out to evaluate the effectiveness of the selected video clips in eliciting the target emotions. The online survey, having on average 220 respondents for each clip, supported the findings.