Locomotion Traces Data Mining for Supporting Frail People with Cognitive Impairment

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of older people. Thus, this thesis at first focused on providing a systematic literature review of locomotion data mining systems for supporting Neuro-Degenerative Diseases (NDD) diagnosis, identifying locomotion anomaly indicators and movement patterns for discovering low-level locomotion indicators, sensor data acquisition, and processing methods, as well as NDD detection algorithms considering their pros and cons. Then, we investigated the use of sensor data and Deep Learning (DL) to recognize abnormal movement patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduced novel visual feature extraction methods for locomotion data. Our solutions rely on locomotion traces segmentation, image-based extraction of salient features from locomotion segments, and vision-based DL. Furthermore, we proposed a data augmentation strategy to increase the volume of collected data and generalize the solution to different smart-homes with different layouts. We carried out extensive experiments with a large real-world dataset acquired in a smart-home test-bed from older people, including people with cognitive diseases. Experimental comparisons show that our system outperforms state-of-the-art methods

Mobile Robotics

The book is a collection of ten scholarly articles and reports of experiences and perceptions concerning pedagogical practices with mobile robotics.“This work is funded by CIEd – Research Centre on Education, project UID/CED/01661/2019, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT.

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

EMG-driven control in lower limb prostheses: a topic-based systematic review

Background The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts. Objective and methodologies This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers. Results and conclusions The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees

Impact of Ear Occlusion on In-Ear Sounds Generated by Intra-oral Behaviors

We conducted a case study with one volunteer and a recording setup to detect sounds induced by the actions: jaw clenching, tooth grinding, reading, eating, and drinking. The setup consisted of two in-ear microphones, where the left ear was semi-occluded with a commercially available earpiece and the right ear was occluded with a mouldable silicon ear piece. Investigations in the time and frequency domains demonstrated that for behaviors such as eating, tooth grinding, and reading, sounds could be recorded with both sensors. For jaw clenching, however, occluding the ear with a mouldable piece was necessary to enable its detection. This can be attributed to the fact that the mouldable ear piece sealed the ear canal and isolated it from the environment, resulting in a detectable change in pressure. In conclusion, our work suggests that detecting behaviors such as eating, grinding, reading with a semi-occluded ear is possible, whereas, behaviors such as clenching require the complete occlusion of the ear if the activity should be easily detectable. Nevertheless, the latter approach may limit real-world applicability because it hinders the hearing capabilities.</p

A new approach to study gait impairments in Parkinson’s disease based on mixed reality

Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Eletrónica Médica)Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. PD onset is at 55 years-old on average, and its incidence increases with age. This disease results from dopamine-producing neurons degeneration in the basal ganglia and is characterized by various motor symptoms such as freezing of gait, bradykinesia, hypokinesia, akinesia, and rigidity, which negatively impact patients’ quality of life. To monitor and improve these PD-related gait disabilities, several technology-based methods have emerged in the last decades. However, these solutions still require more customization to patients’ daily living tasks in order to provide more objective, reliable, and long-term data about patients’ motor conditions in home-related contexts. Providing this quantitative data to physicians will ensure more personalized and better treatments. Also, motor rehabilitation sessions fostered by assistance devices require the inclusion of quotidian tasks to train patients for their daily motor challenges. One of the most promising technology-based methods is virtual, augmented, and mixed reality (VR/AR/MR), which immerse patients in virtual environments and provide sensory stimuli (cues) to assist with these disabilities. However, further research is needed to improve and conceptualize efficient and patient-centred VR/AR/MR approaches and increase their clinical evidence. Bearing this in mind, the main goal of this dissertation was to design, develop, test, and validate virtual environments to assess and train PD-related gait impairments using mixed reality smart glasses, integrated with another high-technological motion tracking device. Using specific virtual environments that trigger PD-related gait impairments (turning, doorways, and narrow spaces), it is hypothesized that patients can be assessed and trained in their daily challenges related to walking. Also, this tool integrates on-demand visual cues to provide visual biofeedback and foster motor training. This solution was validated with end-users to test the identified hypothesis. The results showed that, in fact, mixed reality has the potential to recreate real-life environments that often provoke PD-related gait disabilities, by placing virtual objects on top of the real world. On the contrary, biofeedback strategies did not significantly improve the patients’ motor performance. The user experience evaluation showed that participants enjoyed participating in the activity and felt that this tool can help their motor performance.A doença de Parkinson (DP) é a segunda doença neurodegenerativa mais comum depois da doença de Alzheimer. O início da DP ocorre, em média, aos 55 anos de idade, e a sua incidência aumenta com a idade. Esta doença resulta da degeneração dos neurónios produtores de dopamina nos gânglios basais e é caracterizada por vários sintomas motores como o congelamento da marcha, bradicinesia, hipocinesia, acinesia, e rigidez, que afetam negativamente a qualidade de vida dos pacientes. Nas últimas décadas surgiram métodos tecnológicos para monitorizar e treinar estas desabilidades da marcha. No entanto, estas soluções ainda requerem uma maior personalização relativamente às tarefas diárias dos pacientes, a fim de fornecer dados mais objetivos, fiáveis e de longo prazo sobre o seu desempenho motor em contextos do dia-a-dia. Através do fornecimento destes dados quantitativos aos médicos, serão assegurados tratamentos mais personalizados. Além disso, as sessões de reabilitação motora, promovidas por dispositivos de assistência, requerem a inclusão de tarefas quotidianas para treinar os pacientes para os seus desafios diários. Um dos métodos tecnológicos mais promissores é a realidade virtual, aumentada e mista (RV/RA/RM), que imergem os pacientes em ambientes virtuais e fornecem estímulos sensoriais para ajudar nestas desabilidades. Contudo, é necessária mais investigação para melhorar e conceptualizar abordagens RV/RA/RM eficientes e centradas no paciente e ainda aumentar as suas evidências clínicas. Tendo isto em mente, o principal objetivo desta dissertação foi conceber, desenvolver, testar e validar ambientes virtuais para avaliar e treinar as incapacidades de marcha relacionadas com a DP usando óculos inteligentes de realidade mista, integrados com outro dispositivo de rastreio de movimento. Utilizando ambientes virtuais específicos que desencadeiam desabilidades da marcha (rodar, portas e espaços estreitos), é possível testar hipóteses de que os pacientes possam ser avaliados e treinados nos seus desafios diários. Além disso, esta ferramenta integra pistas visuais para fornecer biofeedback visual e fomentar a reabilitação motora. Esta solução foi validada com utilizadores finais de forma a testar as hipóteses identificadas. Os resultados mostraram que, de facto, a realidade mista tem o potencial de recriar ambientes da vida real que muitas vezes provocam deficiências de marcha relacionadas à DP. Pelo contrário, as estratégias de biofeedback não provocaram melhorias significativas no desempenho motor dos pacientes. A avaliação feita pelos pacientes mostrou que estes gostaram de participar nos testes e sentiram que esta ferramenta pode auxiliar no seu desempenho motor

A quantitative investigation of natural head movement and its contribution to spatial orientation perception.

Movement is ubiquitous in everyday life. As we exist in a physical world, we constantly account for our position in it relative to other physical features: both at a conscious, volitional level and an unconscious one. Our experience estimating our own position accumulates over the lifespan, and it is thought that this experience (often referred to as a prior) informs current perception of spatial orientation. Broadly, this perception of spatial orientation is rapidly performed by the nervous system by monitoring, interpreting, and integrated sensory information from multiple sense organs. To do this efficiently, the nervous system likely represents this sensory information in a statistically optimal manner. Some of the most important information for spatial orientation perception comes from visual and vestibular sensation, which rely on sensory organs located in the head. While statistical information about natural visual and vestibular stimuli have been characterized, natural head movement and position, which likely drives correlated dynamics across head-located senses, has not. Furthermore, sensory cues essential to spatial orientation perception are directly affected by head movement specifically. It is likely that measurement of these sensory cues taken during natural behaviors sample a significant portion of the total behaviors that comprise ones’ prior. In this dissertation, I present work quantifying characteristics of head orientation and heading, two dimensions of spatial orientation, over long-duration recordings of natural behavior in humans. Then, I use these to generate priors for Bayesian modeling frameworks which successfully predict observed patterns of orientation and heading perception bias. Given the ability to predict some patterns of bias (head roll and heading azimuth) particularly well, it is likely our data are representative of real behaviors that comprise previous experience the nervous system may have. Natural head orientation and heading distributions reveal several interesting trends that open future lines of research. First, head pitch demonstrates large amounts of inter-subject variability; likely this is due to biomechanical differences, but as these remain relatively stable over the lifespan these should bias head movements. Second, heading azimuth appears to vary significantly as a function of task. Heading azimuth distributions during low velocities (which predominantly consist of stationary activities like standing or sitting) are strongly multimodal across all subjects, while azimuth distributions during high velocities (predominantly consisting of locomotion) are unimodal with relatively low variance. Future work investigating these trends, as well as implications these trends and data have for sensory processing and other applications is discussed

Proceedings XXII Congresso SIAMOC 2022

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica dà l’occasione a tutti i professionisti, dell’ambito clinico e ingegneristico, di incontrarsi, presentare le proprie ricerche e rimanere aggiornati sulle più recenti innovazioni nell’ambito dell’applicazione clinica dei metodi di analisi del movimento, al fine di promuoverne lo studio e le applicazioni cliniche per migliorare la valutazione dei disordini motori, aumentare l’efficacia dei trattamenti attraverso l’analisi quantitativa dei dati e una più focalizzata pianificazione dei trattamenti, ed inoltre per quantificare i risultati delle terapie correnti