The use of wearable/portable digital sensors in Huntington’s disease: a systematic review

In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington’s disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD

Real-time fMRI neurofeedback and smartphone-based interventions to modulate mental functions

Our brains are constantly changing on a molecular level depending on the demands thrown at them by our environments, behavior, and thoughts. This neuronal plasticity allows us to voluntarily influence mental functions. Taking conscious control over mental functions goes potentially back millenia, but it was psychotherapy since the early 20th century which moulded this concept into a concrete form to target specific mental disorders. Mental disorders constitute a large burden on modern societies. Stress-related disorders like anxiety and depression particularly make up a large part of this burden and new ways to treat or prevent them are highly desirable, since traditional approaches are not equally helpful to every person affected. This might be because the infrastructure is not available where the person lives, their schedules and obligations or financial means do not enable them to seek help or they simply do not respond to traditional forms of treatment. Technological advances bring forth new potential approaches to modulate mental functions and allow using additional information to tailor an intervention better to an individual patient. The focus of this dissertation lies on two promising approaches to cognitively intervene and modulate mental functions: real-time functional magnetic resonance imaging neurofeedback (rtfMRInf) on one hand and smartphone-based interventions (SBIs) on the other. To investigate various aspects of both these methods in the context of stress and in relation to personalized interventions, we designed and conducted two experiments with a main rtfMRInf intervention, and also with ambulatory training of mental strategies, which participants accessed on their mobile phones. The four publication this thesis entails, are related to this topic as follows: The first publication focuses on rtfMRInf effects on the physiological stress response, exploring whether neurofeedback could reduce stress-related changes in brain activity and blood pressure. The second publication focuses on rtfMRInf effects on psychological measures related to the stress response, namely on arousal and mood, based on data from self-report by the participants. The third publication focuses on rtfMRInf methodology itself, looking at complex connectivity data between major neural networks. Finally, the fourth publication focuses on personalized prediction of intervention success of an SBI using data from previous training days

Vibrotactile Sensory Augmentation and Machine Learning Based Approaches for Balance Rehabilitation

Vestibular disorders and aging can negatively impact balance performance. Currently, the most effective approach for improving balance is exercise-based balance rehabilitation. Despite its effectiveness, balance rehabilitation does not always result in a full recovery of balance function. In this dissertation, vibrotactile sensory augmentation (SA) and machine learning (ML) were studied as approaches for further improving balance rehabilitation outcomes. Vibrotactile SA provides a form of haptic cues to complement and/or replace sensory information from the somatosensory, visual and vestibular sensory systems. Previous studies have shown that people can reduce their body sway when vibrotactile SA is provided; however, limited controlled studies have investigated the retention of balance improvements after training with SA has ceased. The primary aim of this research was to examine the effects of supervised balance rehabilitation with vibrotactile SA. Two studies were conducted among people with unilateral vestibular disorders and healthy older adults to explore the use of vibrotactile SA for therapeutic and preventative purposes, respectively. The study among people with unilateral vestibular disorders provided six weeks of supervised in-clinic balance training. The findings indicated that training with vibrotactile SA led to additional body sway reduction for balance exercises with head movements, and the improvements were retained for up to six months. Training with vibrotactile SA did not lead to significant additional improvements in the majority of the clinical outcomes except for the Activities-specific Balance Confidence scale. The study among older adults provided semi-supervised in-home balance rehabilitation training using a novel smartphone balance trainer. After completing eight weeks of balance training, participants who trained with vibrotactile SA showed significantly greater improvements in standing-related clinical outcomes, but not in gait-related clinical outcomes, compared with those who trained without SA. In addition to investigating the effects of long-term balance training with SA, we sought to study the effects of vibrotactile display design on people’s reaction times to vibrational cues. Among the various factors tested, the vibration frequency and tactor type had relatively small effects on reaction times, while stimulus location and secondary cognitive task had relatively large effects. Factors affected young and older adults’ reaction times in a similar manner, but with different magnitudes. Lastly, we explored the potential for ML to inform balance exercise progression for future applications of unsupervised balance training. We mapped body motion data measured by wearable inertial measurement units to balance assessment ratings provided by physical therapists. By training a multi-class classifier using the leave-one-participant-out cross-validation method, we found approximately 82% agreement among trained classifier and physical therapist assessments. The findings of this dissertation suggest that vibrotactile SA can be used as a rehabilitation tool to further improve a subset of clinical outcomes resulting from supervised balance rehabilitation training. Specifically, individuals who train with a SA device may have additional confidence in performing balance activities and greater postural stability, which could decrease their fear of falling and fall risk, and subsequently increase their quality of life. This research provides preliminary support for the hypothesized mechanism that SA promotes the central nervous system to reweight sensory inputs. The preliminary outcomes of this research also provide novel insights for unsupervised balance training that leverage wearable technology and ML techniques. By providing both SA and ML-based balance assessment ratings, the smart wearable device has the potential to improve individuals’ compliance and motivation for in-home balance training.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143901/1/baotian_1.pd

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Influence of Mobile Application Based Brain Training Program on Cognitive Function and Quality of Life in Patients Post Stroke

Background: Stroke often leads to cognitive impairment, which can significantly reduce one's independence as well as quality of life. Cognitive rehabilitation is a treatment strategy for restoring cognitive abilities following brain injuries. Cognitive functioning can be improved through the use of multimedia as well as informatics in computerized cognitive rehabilitation (CCR). Purpose: to investigate the influence of a mobile application-based brain training program on cognitive functions as well as quality of life in post stroke patients. Methodology: forty referred medically and radiologically diagnosed stroke patients from both genders experienced post stroke cognitive impairment (PSCI), aged from 45 to 60 years old, were randomized into two groups of the same number: a study group and a control group. The Study group received mobile application-based brain training program (Lumosity training application) as well as aerobic training on a bicycle ergometer, and the control group received only the aerobic training on a bicycle ergometer for 18 sessions every other day for 6 weeks, 3 sessions/week, each session for 60 minutes. All patients were evaluated with Computer-based cognitive device RehaCom, Addenbrooke’s Cognitive Examination Revised (ACE-R) test, Montreal Cognitive Assessment (MoCA) in addition to Stroke specific quality of life scale (SS-QoL) pre and post treatment. Results: a significant difference has been detected among the two groups as the (p-value = 0.001) indicating that the study group reported enhancement in the cognitive functions as well as the quality of life more than the control group and there was a correlation between RehaCom, MoCA, ACE-R and SS-QoL. Conclusion: This study showed that six weeks of mobile application-based brain training program (Lumosity training application) as well as aerobic training on a bicycle ergometer was a beneficial approach and is a successful treatment for patients suffering from (PSCI)

Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis

Human fatigue due to repetitive and physically challenging jobs may result in poor performance and a Work-related Musculoskeletal Disorder (WMSD). Thus, the importance of being able to monitor fatigue to implement preventative interventions cannot be overstated. This study was designed to monitor fatigue through the development of a methodology that objectively classifies an individual’s level of fatigue in the workplace by utilizing the motion sensors embedded in smartphones. An experiment consisting of squatting tasks, primarily involving the lower extremity musculature, was conducted with 24 participants using a smartphone attached to their right shank. Using Borg’s Ratings of Perceived Exertion (RPE) to label gait data, we developed machine learning algorithms to classify each individual’s gait into two (no- vs. strong-fatigue), three (no-, medium-, and strong-fatigue) and four (no-, low-, medium-, and strong-fatigue) levels of fatigue, for which accuracy of 91%, 76%, and 61% were obtained, respectively. The outcomes of this study may facilitate the implementation of a proactive approach supporting the continuous monitoring of a worker’s fatigue level, which may subsequently enhance workers’ performance and reduce the risk of WMSDs

PD_manager: an mHealth platform for Parkinson's disease Management

Parkinson’s disease (PD) current clinical management is mostly based on patient’s subjective report about the effects of treatments and on medical examinations that unfortunately represent only a snapshot of a highly fluctuating clinical condition. This traditional approach requires time, it is biased by patient’s judgment and is often not completely reliable, especially in moderate advanced stages. The main purpose of the EU funded project PD_manager (Horizon 2020, Grant Agreement n° 643706) is to build and evaluate an innovative, mHealth, patient-centric system for PD remote monitoring. After a first phase of research and development, a set of wearable devices has been selected and tested on 20 patients. The raw data recorded have been used to feed algorithms necessary to recognize motor symptoms. In parallel, other applications have been developed to test also the main non-motor symptoms. On a second phase, a case- control randomized multicentric study has been designed and performed to assess the acceptability and utility of the PD_manager system at patients’ home, compared to the current gold standard for home monitoring, represented by symptoms diaries. 136 couples of patients and caregivers have been recruited, and at the end of the trial the system was found to be very well tolerated and easy to use, compared to diaries. The developed System is able to recognize motor and non-motor symptoms, helping healthcare professionals in taking decisions on therapeutic strategies. Moreover, PD_manager could represent a useful tool for patient's self-monitoring and self-care promotion

Understanding disease through remote monitoring technology:A mobile health perspective on disease and diagnosis in three conditions: stress, epilepsy, and COVID-19

Mobile systems and wearable technology have developed substantially over the last decade and provide a unique long-term and continuous insight and monitoring into medical condi- tions in health research. The opportunities afforded by mobile health in access, scale, and round-the-clock recording are counterbalanced by pronounced issues in areas like participant engagement, labelling, and dataset size. Throughout this thesis the different aspects of an mHealth study are addressed, from software development and study design to data collection and analysis. Three medically relevant fields are investigated: detection of stress from physiological signals, seizure detection in epilepsy and the characterisation and monitoring of COVID-19 through mobile health techniques.The first two analytical chapters of the thesis focus on models for acute stress and epileptic seizure detection, two conditions with autonomic and physiological manifestations. Firstly, a multi-modal machine learning pipeline is developed targetting focal and general motor seizures in patients with epilepsy. The heterogenity and inter-individual differences present in this study motivated the investigation of methods to personalise models with relatively little data. I subsequently consider meta-learning for few-shot model personalisation within acute stress classification, finding increased performance compared to standard methods.As the COVID-19 pandemic gripped the world the work of this thesis reoriented around using mHealth to understand the disease. Firstly, the study design and software development of Covid Collab, a crowdsourced, remote-enrollment COVID-19 study, are examined. Within these chapters, the patterns of participant enrolment and adherence in Covid Col- lab are also considered. Adherence could impact scientific interpretations if not properly accounted for. While basic drop-out and percent completion are often considered, a more dynamic view of a participant’s behaviour can also be important. A hidden Markov model approach is used to compare participant engagement over time.Secondly, the long-term effects of COVID are investigated through data collected in the Covid Collab study, giving insight into prevalence, risk factors, and symptom manifestation with respect to wearable-recorded physiological signals. Long-term and historical data accessed retrospectively facilitated the findings of significant correlations between development of long-COVID and mHealth-derived fitness and behaviour

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