Evaluation of Wearable Electronics for Epilepsy: A Systematic Review

Epilepsy is a neurological disorder that affects 50 million people worldwide. It is characterised by seizures that can vary in presentation, from short absences to protracted convulsions. Wearable electronic devices that detect seizures have the potential to hail timely assistance for individuals, inform their treatment, and assist care and self-management. This systematic review encompasses the literature relevant to the evaluation of wearable electronics for epilepsy. Devices and performance metrics are identified, and the evaluations, both quantitative and qualitative, are presented. Twelve primary studies comprising quantitative evaluations from 510 patients and participants were collated according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Two studies (with 104 patients/participants) comprised both qualitative and quantitative evaluation components. Despite many works in the literature proposing and evaluating novel and incremental approaches to seizure detection, there is a lack of studies evaluating the devices available to consumers and researchers, and there is much scope for more complete evaluation data in quantitative studies. There is also scope for further qualitative evaluations amongst individuals, carers, and healthcare professionals regarding their use, experiences, and opinions of these devices

A usability study of physiological measurement in school using wearable sensors

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps

A Multi-Sensor Platform for Microcurrent Skin Stimulation during Slow Wave Sleep

Insu cient and low quality sleep is related to several health issues and social outcomes. Regular sleep study conducted in a sleep laboratory is impractical and expensive. As a result, miniature and non-invasive sleep monitoring devices provide an accessible sleep data. Though not as accurate as polysomnography, these devices provide useful data to the subject by tracking sleep patterns regularly. On the other hand, proactive improvement of sleep quality has been limited to pharmacological solutions and cranial electrotherapy stimulation. An alternative approach and a potential solution to sleep deprivation is a non-pharmacological technique which involves the application of micro-current electrical stimulation on the palm during Slow Wave Sleep (SWS). This thesis presents the development of a miniature device for SWS detection and electrocutaneous stimulation. Several sensors are embedded in the prototype device to measure physiological data such as body movement, electrodermal activity, heart rate, and skin and ambient temperature. Furthermore, the prototype device provides local storage and wireless transfer for data acquisition. The quality of the sensor data during sleep are discussed in this thesis. For future work, the results of this thesis shall be the used as a baseline to develop a more re ned prototype for clinical trials in sleep laboratories

Tunteiden Havaitseminen Arkielämässä Koneoppimisen ja Puettavien Laitteiden Avulla

Tavoitteet. Tämän tutkimuksen tavoitteena on arvioida tunteiden havaitsemisen mahdollisuutta arkielämässä puettavien laitteiden ja koneoppimismallien avulla. Tunnetiloilla on tärkeä rooli päätöksenteossa, havaitsemisessa ja käyttäytymisessä, mikä tekee objektiivisesta tunnetilojen havaitsemisesta arvokkaan tavoitteen, sekä mahdollisten sovellusten että tunnetiloja koskevan ymmärryksen syventämisen kannalta. Tunnetiloihin usein liittyy mitattavissa olevia fysiologisia ja käyttäymisen muutoksia, mikä mahdollistaa koneoppimismallien kouluttamisen muutoksia aiheuttaneen tunnetilan havaitsemiseksi. Suurin osa tunteiden havaitsemiseen liittyvästä tutkimuksesta on toteutettu laboratorio-olosuhteissa käyttämällä tunteita herättäviä ärsykkeitä tai tehtäviä, mikä herättää kysymyksen siitä että yleistyvätkö näissä olosuhteissa saadut tulokset arkielämään. Vaikka puettavien laitteiden ja kännykkäkyselyiden kehittyminen on helpottanut aiheen tutkimista arkielämässä, tutkimusta tässä ympäristössä on vielä niukasti. Tässä tutkimuksessa itseraportoituja tunnetiloja ennustetaan koneoppimismallien avulla arkielämässä havaittavissa olevien tunnetilojen selvittämiseksi. Lisäksi tutkimuksessa käytetään mallintulkintamenetelmiä mallien hyödyntämien yhteyksien tunnistamiseksi. Metodit. Aineisto tätä tutkielmaa varten on peräisin tutkimuksesta joka suoritettiin osana Helsingin Yliopiston ja VTT:n Sisu at Work projektia, missä 82:ta tietotyöläistä neljästä suomalaisesta organisaatiosta tutkittiin kolmen viikon ajan. Osallistujilla oli jakson aikana käytettävissään mittalaitteet jotka mittasivat fotoplethysmografiaa (PPG), ihon sähkönjohtavuutta (EDA) ja kiihtyvyysanturi (ACC) signaaleita, lisäksi heille esitettiin kysymyksiä koetuista tunnetiloista kolmesti päivässä puhelinsovelluksen avulla. Signaalinkäsittelymenetelmiä sovellettiin signaaleissa esiintyvien liikeartefaktien ja muiden ongelmien korjaamiseksi. Sykettä (HR) ja sykevälinvaihtelua (HRV) kuvaavia piirteitä irroitettiin PPG signaalista, fysiologista aktivaatiota kuvaavia piirteitä EDA signaalista, sekä liikettä kuvaavia piirteitä ACC signaalista. Seuraavaksi koneoppimismalleja koulutettiin ennustamaan raportoituja tunnetiloja irroitetujen piirteiden avulla. Mallien suoriutumista vertailtiin suhteessa odotusarvoihin havaittavissa olevien tunnetilojen määrittämiseksi. Lisäksi permutaatiotärkeyttä sekä Shapley additive explanations (SHAP) arvoja hyödynnettiin malleille tärkeiden yhteyksien selvittämiseksi. Tulokset ja johtopäätökset. Mallit tunnetiloille virkeä, keskittynyt ja innostunut paransivat suoriutumistaan yli odotusarvon, joista mallit tunnetilalle virkeä paransivat suoriutumista tilastollisesti merkitsevästi. Permutaatiotärkeys korosti liike- ja HRV-piirteiden merkitystä, kun SHAP arvojen tarkastelu nosti esiin matalan liikkeen, matalan EDA:n, sekä korkean HRV:n merkityksen mallien ennusteille. Nämä tulokset ovat lupaavia korkean aktivaation positiivisten tunnetilojen havaitsemiselle arkielämässä, sekä nostavat esiin mahdollisia yhteyksiä jatkotutkimusta varten.Objectives. This study aims to evaluate feasibility of affect detection in daily life using wearable devices and machine learning models. Affective states play an important role in decision making, perception and behaviour, making objective detection of affective states a desirable goal both for potential applications and as a way to gain insight into affective phenomena. Affective states have been found to have measurable physiological and behavioral changes, which allows training of machine learning models for detecting the underlying affects. Majority of affect detection studies have been conducted in laboratory conditions using affect elicitation stimuli or tasks, raising the question whether results from these studies will generalize to daily life. Although development of wearable devices and mobile surveys have facilitated evaluation in the context of daily life, research here remains sparse. In this study, self-reported affective states are predicted using machine learning models to identify which affective states can be detected in daily life. Additionally, model interpretation methods will be used to identify which relationships the models found important for their predictions. Methods. Data for this thesis came from a study conducted as a part of Sisu at Work project between University of Helsinki and VTT, where 82 knowledge workers from four Finnish organizations were studied for a period of three weeks. During this period, the participants were queried by mobile surveys about their affective states thrice a day, while they also used wearable devices to record photoplethysmography (PPG), electrodermal activity (EDA) and accelerometry (ACC) signals. A signal processing pipeline was implemented to deal with movement artefacts and other issues with the data. Features describing heart rate (HR) and heart rate variation (HRV) were extraced from PPG, physiological activation from EDA and movement from ACC signals. Models were then fitted to predict the reported affective states using the extracted features. Model performance was compared against a baseline to identify which affects could be reliably detected, while permutation importance and Shapley additive explanations (SHAP) values were used to identify important relationships established by the models. Results and conclusions. Models for affective state vigor showed improvements over baseline with statistical significance, while improvements were also noted for affects focused and enthusiastic. Permutation importance highlighted the significance of movement and HRV features, while examination of SHAP values indicated that low movement, low EDA and high HRV impacted model predictions the most. These results indicate potential for detecting high activation affective states in daily life and propose potential relationships for future research

Physiopad: development of a non-invasive game controller toolkit to study physiological responses for Game User Research

Os jogos afectivos usam as respostas fisiológicas do jogador para criar um ambiente adequado ao estado emocional do utilizador. A investigação destes jogos tem sido explorada nos últimos anos. Estas experiências, contudo, ainda requerem sistemas complexos e difíceis de utilizar. Nesta dissertação, é proposta a construção de um dispositivo capaz de ler dados fisiológicos de forma não invasiva e que seja de fácil utilização. Este aparelho faz a leitura do ritmo cardíaco e dos níveis de excitação do jogador, além disso foi criado um software para interligar com o dispositivo. Utilizando um comando da PlayStation 3 e um BITalino, o dispositivo é capaz de fazer a aquisição do sinal PPG e sinal EDA durante o jogo. O software analisa os sinais do comando, calcula o ritmo cardíaco e mede os níveis de excitação em tempo real. Foi realizada uma experiência utilizando biofeedback positivo e negativo, com o objectivo de testar a integração entre o software e o hardware. Não será no imediato que os dispositivos deste género sejam disponibilizados comercialmente. Os resultados são, no entanto, promissores. O cálculo do ritmo cardíaco em tempo real tem apenas uma diferença de 5 batimentos por minuto em relação ao ritmo cardíaco real do jogador. Apesar de os testes com o EDA serem inconclusivos, pode-se verificar que foi possível construir um sistema para ler os dados fisiológicos sendo mais económico do que os seus pares, sem comprometer a fiabilidade dos dados.Affective games are a genre of games that use the physiological responses from the player to adapt the gameplay to a more enjoyable emotional state and experience. Physiological responses and affective games have been studied vastly over the years. However, the setups used in these interventions are very intrusive and are complex to set up. In this project, it is purposed to build a non-invasive and easy-to-set-up toolkit that records physiological data. This toolkit records the player's heart rate and arousal levels and was decomposed into software and hardware. Using a PS3 game controller replica and a BITalino, a physiological game controller which can record heart rate and arousal during gameplay was built. The software interfaces with the gamepad, processes the physiological signals and sends this information to the game. An experiment with a positive biofeedback condition and negative biofeedback condition was conducted. This experiment showed that even though more work must be done until these type of devices could be commercially available, the results are promising. This toolkit’s heart rate values, when compared with other more traditional acquisition devices, were very similar, being on average only 5 BMP lower than the actual heart rate, proving that is possible to build more affordable non-invasive physiological hardware without compromising the signal's accuracy

Autonomic arousal and attentional orienting to visual threat are predicted by awareness

The rapid detection and evaluation of threat is of fundamental importance for survival. Theories suggest that this evolutionary pressure has driven functional adaptations in a specialized visual pathway that evaluates threat independently of conscious awareness. This is supported by evidence that threat-relevant stimuli rendered invisible by backward masking can induce physiological fear responses and modulate spatial attention. The validity of these findings has since been questioned by research using stringent, objective measures of awareness. Here, we use a modified continuous flash suppression paradigm to ask whether threatening images induce adaptive changes in autonomic arousal, attention, or perception when presented outside of awareness. In trials where stimuli broke suppression to become visible, threatening stimuli induced a significantly larger skin conductance response than nonthreatening stimuli and attracted spatial attention over scrambled images. However, these effects were eliminated in trials where observers were unaware of the stimuli. In addition, concurrent behavioral data provided no evidence that threatening images gained prioritized access to awareness. Taken together, our data suggest that the evaluation and spatial detection of visual threat are predicted by awareness

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

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans

A psychophysiological insight into driver state during highly automated driving

The aim of this research was to investigate and validate the usage of physiological measures as an objective indicator of driver state in dynamic driving environments, and understand if such a methodology can be used to measure driver discomfort, and high workload. The work addressed questions relating to: (i) detecting and removing motion artefacts from electrodermal activity (EDA) signals in dynamic driving environments; (ii) primary factors contributing to driver discomfort during automation, measured in terms of their physiological state; (iii) understanding changes in drivers’ workload levels at different stages of automation, as indicated by electrocardiogram (ECG) and EDA-based measures and; (iv) how drivers’ attentional demands and workload levels are affected at different stages of automation, measured using eye tracking-based metrics. A series of experiments were developed to manipulate drivers’ discomfort and workload levels. The analysis around driver discomfort focused on automated driving, whereas drivers’ workload levels were investigated during automation, and during resumption of control from automation, in a series of car-following scenarios. Our results indicated that phasic EDA was able to pick up discomfort experienced by the driver during automation, and correlated to drivers’ subjective ratings of discomfort. Narrower roads, higher resultant acceleration forces and how the automated vehicle negotiated different road geometries all influenced driver discomfort. We observed that drivers’ workload levels were captured by ECG and EDA-based signals, with phasic component of EDA signal being more sensitive to short term variations in driver workload. Similar results were observed in drivers’ pupil diameter values, as well as subjective ratings of workload. Factors such as engagement in a non-driving related task (NDRT), presence of a lead vehicle while maintaining a short time headway, and takeovers, all seemed to increase drivers’ workload levels. Future work can build on this research by incorporating sensor fusion of ECG and EDA-based data, along with eye tracking, to help improve the accuracy and capabilities of future driver state monitoring systems