Skin Admittance Measurement for Emotion Recognition: A Study over Frequency Sweep

The electrodermal activity (EDA) is a reliable physiological signal for monitoring the sympathetic nervous system. Several studies have demonstrated that EDA can be a source of effective markers for the assessment of emotional states in humans. There are two main methods for measuring EDA: endosomatic (internal electrical source) and exosomatic (external electrical source). Even though the exosomatic approach is the most widely used, differences between alternating current (AC) and direct current (DC) methods and their implication in the emotional assessment field have not yet been deeply investigated. This paper aims at investigating how the admittance contribution of EDA, studied at different frequency sources, affects the EDA statistical power in inferring on the subject?s arousing level (neutral or aroused). To this extent, 40 healthy subjects underwent visual affective elicitations, including neutral and arousing levels, while EDA was gathered through DC and AC sources from 0 to 1 kHz. Results concern the accuracy of an automatic, EDA feature-based arousal recognition system for each frequency source. We show how the frequency of the external electrical source affects the accuracy of arousal recognition. This suggests a role of skin susceptance in the study of affective stimuli through electrodermal response

Detecting stressful older adults-environment interactions to improve neighbourhood mobility: A multimodal physiological sensing, machine learning, and risk hotspot analysis-based approach

Not only is the global population ageing, but also the built environment infrastructure in many cities and communities are approaching their design life or showing significant deterioration. Such built environment conditions often become an environmental barrier that can either cause stress and/or limit the mobility of older adults in their neighbourhood. Current approaches to detecting stressful environmental interactions are less effective in terms of time, cost, labour, and individual stress detection. This study harnesses the recent advances in wearable sensing technologies, machine learning intelligence and hotspot analysis to develop and test a more efficient approach to detecting older adults' stressful interactions with the environment. Specifically, this study monitored older adults' physiological reactions (Photoplethysmogram and electrodermal activity) and global positioning system (GPS) trajectory using wearable sensors during an outdoor walk. Machine learning algorithms, including Gaussian Support Vector Machine, Ensemble bagged tree, and deep belief network were trained and tested to detect older adults' stressful interactions from their physiological signals, location and environmental data. The Ensemble bagged tree achieved the best performance (98.25% accuracy). The detected stressful interactions were geospatially referenced to the GPS data, and locations with high-risk clusters of stressful interactions were detected as risk stress hotspots for older adults. The detected risk stress hotspot locations corresponded to the places the older adults encountered environmental barriers, supported by site inspections, interviews and video records. The findings of this study will facilitate a near real-time assessment of the outdoor neighbourhood environment, hence improving the age-friendliness of cities and communities

Wearable sensing and mining of the informativeness of older adults : physiological, behavioral, and cognitive responses to detect demanding environmental conditions

Due to the decline in functional capability, older adults are more likely to encounter excessively demanding environmental conditions (that result in stress and/or mobility limitation) than the average person. Current efforts to detect such environmental conditions are inefficient and are not person-centered. This study presents a more efficient and person-centered approach that involves using wearable sensors to collect continuous bodily responses (i.e., electroencephalography, photoplethysmography, electrodermal activity, and gait) and location data from older adults to detect demanding environmental conditions. Computationally, this study developed a Random Forest algorithm—considering the informativeness of the bodily response—and a hot spot analysis-based approach to identify environmental locations with high demand. The approach was tested on data collected from 10 older adults during an outdoor environmental walk. The findings demonstrate that the proposed approach can detect demanding environmental conditions that are likely to result in stress and/or limited mobility for older adults

Novel Machine Learning and Wearable Sensor Based Solutions for Smart Healthcare Monitoring

The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These health monitoring systems can be utilized for monitoring the mental and physical wellbeing of a person. Stress, anxiety, and hypertension are the major elements responsible for the plethora of physical and mental illnesses. In this context, the older population demands special attention because of the several age-related complications that exacerbate the effects of stress, anxiety, and hypertension. Monitoring stress, anxiety, and blood pressure regularly can prevent long-term damage by initiating necessary intervention or clinical treatment beforehand. This will improve the quality of life and reduce the burden on caregivers and the cost of healthcare. Therefore, this thesis explores novel technological solutions for real-time monitoring of stress, anxiety, and blood pressure using unobtrusive wearable sensors and machine learning techniques. The first contribution of this thesis is the experimental data collection of 50 healthy older adults, based on which, the works on stress detection and anxiety detection have been developed. The data collection procedure lasted for more than a year. We have collected physiological signals, salivary cortisol, and self-reported questionnaire feedback during the study. Salivary cortisol is an established clinical biomarker for physiological stress. Hence, a stress detection model that is trained to distinguish between the stressed and not-stressed states as indicated by the increase in cortisol level has the potential to facilitate clinical level diagnosis of stress from the comfort of their own home. The second contribution of the thesis is the development of a stress detection model based on fingertip sensors. We have extracted features from Electrodermal Activity (EDA) and Blood Volume Pulse (BVP) signals obtained from fingertip EDA and Photoplethysmogram (PPG) sensors to train machine learning algorithms for distinguishing between stressed and not-stressed states. We have evaluated the performance of four traditional machine learning algorithms and one deep-learning-based Long Short-Term Memory (LSTM) classifier. Results and analysis showed that the proposed LSTM classifier performed equally well as the traditional machine learning models. The third contribution of the thesis is to evaluate an integrated system of wrist-worn sensors for stress detection. We have evaluated four signal streams, EDA, BVP, Inter-Beat Interval (IBI), and Skin Temperature (ST) signals from EDA, PPG, and ST sensors. A random forest classifier was used for distinguishing between the stressed and not-stressed states. Results and analysis showed that incorporating features from different signals was able to reduce the misclassification rate of the classifier. Further, we have also prototyped the integration of the proposed wristband-based stress detection system in a consumer end device with voice capabilities. The fourth contribution of the thesis is the design of an anxiety detection model that uses features from a single wearable sensor and a context feature to improve the performance of the classification model. Using a context feature instead of integrating other physiological features for improving the performance of the model can reduce the complexity and cost of the anxiety detection model. In our proposed work, we have used a simple experimental context feature to highlight the importance of context in the accurate detection of anxious states. Our results and analysis have shown that with the addition of the context-based feature, the classifier was able to reduce misclassification by increasing the confidence of the decision. The final and the fifth contribution of the thesis is the validation of a proposed computational framework for the blood pressure estimation model. The proposed framework uses features from the PPG signal to estimate the systolic and diastolic blood pressure values using advanced regression techniques

Automatic detection of disorientation among people with dementia

Ageing is characterized by decline in cognition including visuospatial function, necessary for independently executing instrumental activities of daily living. The onset of Alzheimer’s disease dementia exacerbates this decline, leading to major challenges for patients and increased burden for caregivers. An important function affected by this decline is spatial orientation. This work provides insight into substrates of real-world wayfinding challenges among older adults, with emphasis on viable features aiding the detection of spatial disorientation and design of possible interventions

Low-cost methodologies and devices applied to measure, model and self-regulate emotions for Human-Computer Interaction

En aquesta tesi s'exploren les diferents metodologies d'anàlisi de l'experiència UX des d'una visió centrada en usuari. Aquestes metodologies clàssiques i fonamentades només permeten extreure dades cognitives, és a dir les dades que l'usuari és capaç de comunicar de manera conscient. L'objectiu de la tesi és proposar un model basat en l'extracció de dades biomètriques per complementar amb dades emotives (i formals) la informació cognitiva abans esmentada. Aquesta tesi no és només teòrica, ja que juntament amb el model proposat (i la seva evolució) es mostren les diferents proves, validacions i investigacions en què s'han aplicat, sovint en conjunt amb grups de recerca d'altres àrees amb èxit.En esta tesis se exploran las diferentes metodologías de análisis de la experiencia UX desde una visión centrada en usuario. Estas metodologías clásicas y fundamentadas solamente permiten extraer datos cognitivos, es decir los datos que el usuario es capaz de comunicar de manera consciente. El objetivo de la tesis es proponer un modelo basado en la extracción de datos biométricos para complementar con datos emotivos (y formales) la información cognitiva antes mencionada. Esta tesis no es solamente teórica, ya que junto con el modelo propuesto (y su evolución) se muestran las diferentes pruebas, validaciones e investigaciones en la que se han aplicado, a menudo en conjunto con grupos de investigación de otras áreas con éxito.In this thesis, the different methodologies for analyzing the UX experience are explored from a user-centered perspective. These classical and well-founded methodologies only allow the extraction of cognitive data, that is, the data that the user is capable of consciously communicating. The objective of this thesis is to propose a methodology that uses the extraction of biometric data to complement the aforementioned cognitive information with emotional (and formal) data. This thesis is not only theoretical, since the proposed model (and its evolution) is complemented with the different tests, validations and investigations in which they have been applied, often in conjunction with research groups from other areas with success

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) PI15/00306Junta de Andalucía PIN-0394-2017Unión Europea "FRAIL

Investigation Into the Physical Environmental Correlates of Aggressive Behaviour in Children with Neurodevelopmental Disorders (NDDs)

Background: Physical environmental influences on childhood aggression in children with neurodevelopmental disabilities is a severely under-researched research locus. The aim of this doctorate was to elucidate specific associations between children’s developmental environment and aggressive behaviours, using this evidence to reciprocally inform an experimental psychology project to investigate underlying mechanisms. To explore these effects, the programme of study was broadly divided into three reflexive workstreams using diverse research methodologies. Methods: In the first workstream, I conducted a systematic review of the current literature examining physical environmental influences on childhood aggressive behaviours in both typically developing children (aged 0 – 18) and those diagnosed with NDDs. The literature on children with NDDs was substantially limited in comparison to peers without NDDs. The second workstream was comprised of a large-scale secondary data analysis (multiply imputed growth curve modelling) to investigate environmental influences on conduct problems across early development. I used data from the Millennium Cohort Study (MCS) to assess how physical environmental metrics, such as neighbourhood greenspace, air pollution, household crowding, and presence of home damp influenced the development and severity of conduct problems in children with (n=8013) and without NDDs (n=155) between the ages of 3 – 11 years. Finally, building upon evidence from the previous two workstreams, I designed a proof-of-principle psychological experiment to examine the influence of urban nature exposure on children with NDDs. Specifically, simulating a real-world urban greenspace using a Person-Environment-Activity Research Laboratory (PEARL). This facilitated the ability to manipulate and isolate individual environmental aspects of urban nature exposure (light, sound, and projection). Following ethical review and approval, I recruited 3 children (100% male) with mild and moderate intellectual disability aged between 12 – 15 years (Mean age = 14) attending a local school for children with special educational needs. We examined their physiological reactions to four simulated urban green space aspects (light, sound, landscape projections, and vegetation) against a baseline control condition. I also collected demographic information on parent reported aggressive behaviours, exposure to local greenspace(s), physical and mental health history, medication, and adaptive behaviours (ABAS-3). This research lays the foundation for future large scale experimental paradigms that can disentangle the effects of nature exposure in these children, with the aim of translating these findings into real world therapeutic design interventions and relevant policy changes to improve the quality of the built environment for these children. Findings: From articles retrieved from my systematic review I found evidence for the beneficial influences of nature in both populations, and simultaneously negative effects of both noise and air pollution in typically developing children only. Evidence for other environmental aspects such as crowding, music, urbanicity, meteorology, and interior design had either insufficient or inconsistent evidence to extrapolate concreate conclusions. More evidence on the effect of these exposures on child aggression outcomes is recommended. From the analysis of the MCS cohort I found various sociodemographic factors (ethnicity, sex, poverty, family structure, maternal distress) and internal residential conditions were associated with increased childhood conduct problem trajectories in both groups of children. I also discovered potential evidence of a moderating influence effect of intellectual disability on the relationship between spatial density and conduct problems. From the final experimental project, I report preliminary evidence for the influence of urban greenspaces to reduce physiological arousal in children with complex neurodisability profiles. Initial evidence for the hierarchical nature of urban greenspace sensorial aspects was reported, for example: that urban nature soundscapes maybe a more influential environmental stimuli than lighting or landscape projections. Conclusion: Drawing together multi-disciplinary research methodologies facilitated the ability to identify disparities in research examining physical environmental determinants of aggression in neurodiverse child populations. Reciprocally, the systematic review and secondary data analysis contributed incrementally to filling this lacuna of research. Using findings from these two work streams, I identified that exploring the potentially therapeutic influences of urban nature exposure on children with neurodevelopmental disorders may provide novel indicators of its aetiological mechanisms. I reported original findings supporting these research aims, elucidating the potential hierarchical nature of urban greenspace elements. This was also the first study of its kind reporting the potential for simulated urban park spaces to reduce physiological arousal in neurodivergent children with aggressive behavioural difficulties

THE DEVELOPMENT OF THE PALMAR SWEAT INDEX AS AN APPLIED MEASURE IN CLINICAL PSYCHOLOGY

Five studies are described, examining the validity of the Palmar Sweat Index (PSI) as an alternative to traditional measures of electrodermal activity (EDA). A review of research using the PSI identified tour topics which needed to be addressed, before the PSI could be accepted as an alternative to measures of EDA. The first of these topics concerns the reliability of the PSI. A preliminary analysis confirmed that the PSI could be scored reliably. The second topic to be examined, was the relationship between the PSI and measures of EDA. The PSI was found to correlate significantly with several parameters of EDA. These results provide some support for models of EDA involving a single effector. More importantly, the PSI response was observed to show rapid recovery, and in one study the PSI was observed to show adaptation over the course of the session, while skin conductance level did not. The difference in the temporal patterning of the responses shown by the two measures provides an explanation for previous reports of a dissociation between the PSI and EDA. The final topics to be examined concerned the effects of psychological stress and anxiety, respectively, on the PSI. Stressful cognitive tasks were observed to lead to increased palmar sweating. Previous claims that the PSI may decrease in response to stress were not supported. More ecologically-valid stressors were less consistently associated with elevated levels of sweat gland activity. There was some support for a relationship between the PSI and experienced anxiety. It is suggested that this may explain the raised sweat gland activity observed during stressful tasks. Data are also presented from three collaborative studies. This data was collected by other workers and demonstrates the utility of the PSI for applied clinical research