13 research outputs found

    Development of Waterloo Robotic Rollator (WATRR)

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    One of the major risk factors for impaired mobility is aging, and with the aging population on the rise, the demand for assistive technologies for individuals with mobility impairment is at an all time high. Impaired mobility can lead to loss of independence, increased chance of mortality, deterioration of health, decreased cognitive function and a poor quality of life. Moreover, individuals with impaired mobility also tend to have higher hospital utilization costs. Mobility capability can be (re)built through the use of assistive technologies. Rollators/Walkers are a commonly used mobility aid that has shown to help with mobility by providing support, particularly transferring a portion of the lower limb loads to the upper limbs. However, safety has been a concern with rollators, with thousands of accidents occurring every year. Currently, many research projects are investigating methods to improve rollators, particularly surrounding the use of robotic rollators. At University of Waterloo Neural and Rehabilitation lab (NRE lab), our goal is to develop technology to improve lives of people, with development of robotic rollators being one of our research foci. The Waterloo Robotic Rollators (WATRR) is an active rollator system with built-in sensing and actuation systems. It is believed that the user experience and safety of rollators can be improved through the use of smart control algorithms. The purpose of this thesis was to develop methods to address safety and user experience concerns by proposing a hybrid control approach, where distance and orientation control are key control parameters, including automatic braking. First, the Waterloo Robotic Rollator (WATRR), a low weight robotic rollator platform, representative of current rollators with sensors and actuators is presented. I describe key design decisions for the platform, offer an overview of the software architecture, and discuss further research development goals. The proposed hybrid controller is then described and both simulation and experimental data for controller design is presented. To enable the envisioned hybrid control systems, a human state estimator and a robot state estimator are required. The human state estimator uses computer vision and machine learning in a hourglass network structure to predict shoulder locations. Using the estimated location and depth data, human velocity, distance and orientation relative to the rollator are estimated. For the robot state estimator, a new velocity estimator based on learning methods is proposed. As rollator lateral velocity can be difficult to estimate with traditional methods, we propose an augmented learning-aided state estimator. This estimator is a Long- Short-Term Memory (LSTM) based estimator, augmented with an Unscented Kalman Filter (UKF). The proposed estimator was validated through experimental data. The main contribution of this thesis was a new lightweight rollator system with sensors and actuators that enabled development of advanced controls. Next, previous control systems are not only improved upon by using a new hybrid controller but also implemented on our platform. A new robot state estimator is developed that relies solely on the kinematics and is able to estimate lateral velocity with a mean error of <10mm/s<10mm/s without requiring additional instrumentation or knowledge of the rollator's time varying parameters. Finally, a new human state estimator is designed which does not require instrumentation on the human and outperforms current estimators

    A deep learning solution for real-time human motion decoding in smart walkers

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    Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Eletrónica Médica)The treatment of gait impairments has increasingly relied on rehabilitation therapies which benefit from the use of smart walkers. These walkers still lack advanced and seamless Human-Robot Interaction, which intuitively understands the intentions of human motion, empowering the user’s recovery state and autonomy, while reducing the physician’s effort. This dissertation proposes the development of a deep learning solution to tackle the human motion decoding problematic in smart walkers, using only lower body vision information from a camera stream, mounted on the WALKit Smart Walker, a smart walker prototype for rehabilitation purposes. Different deep learning frameworks were designed for early human motion recognition and detec tion. A custom acquisition method, including a smart walker’s automatic driving algorithm and labelling procedure, was also designed to enable further training and evaluation of the proposed frameworks. Facing a 4-class (stop, walk, turn right/left) classification problem, a deep learning convolutional model with an attention mechanism achieved the best results: an offline f1-score of 99.61%, an online calibrated instantaneous precision higher than 97% and a human-centred focus slightly higher than 30%. Promising results were attained for early human motion detection, with enhancements in the focus of the proposed architectures. However, further improvements are still needed to achieve a more reliable solution for integration in a smart walker’s control strategy, based in the human motion intentions.O tratamento de distúrbios da marcha tem apostado cada vez mais em terapias de reabilitação que beneficiam do uso de andarilhos inteligentes. Estes ainda carecem de uma Interação Humano-Robô avançada e eficaz, capaz de entender, intuitivamente, as intenções do movimento humano, fortalecendo a recuperação autónoma do paciente e reduzindo o esforço médico. Esta dissertação propõe o desenvolvimento de uma solução de aprendizagem para o problema de descodificação de movimento humano em andarilhos inteligentes, usando apenas vídeos recolhidos pelo WALKit Smart Walker, um protótipo de andarilho inteligente usado para reabilitação. Foram desenvolvidos algoritmos de aprendizagem para o reconhecimento e detecção precoces de movimento humano. Um método de aquisição personalizado, incluindo um algoritmo de condução e labelização automatizados, foi projetado para permitir o conseguinte treino e avaliação dos algoritmos propostos. Perante a classificação de 4 ações (parar, andar, virar à direita/esquerda), um modelo convolucional com um mecanismo de atenção alcançou os melhores resultados: f1-score offline de 99,61%, precisão instantânea calibrada online de superior a 97 % e um foco centrado no ser humano ligeiramente superior a 30%. Com esta dissertação alcançaram-se resultados promissores para a detecção precoce de movimento humano, com aprimoramentos no foco dos algoritmos propostos. No entanto, ainda são necessárias melhorias adicionais para alcançar uma solução mais robusta para a integração na estratégia de controlo de um andarilho inteligente, com base nas intenções de movimento do utilizador

    Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

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    The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy

    Nutzerorientierte Evaluation zweier altersgerechter Assistenzroboter zur Unterstützung von Alltagsaktivitäten („Ambient Assisted Living-Roboter“) bei älteren Menschen mit funktionellen Einschränkungen: MOBOT-Rollator und I-SUPPORT-Duschroboter

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    Ziel der vorliegenden Arbeit ist die nutzerorientierte Evaluation zweier Prototypen für altersgerechte Assistenzroboter zur Unterstützung von Alltagsaktivitäten („Ambient Assisted Living“ [AAL]-Roboter) bei älteren Menschen mit funktionellen Einschränkungen. Bei den Prototypen handelt es sich dabei um (1) einen robotergestützten Rollator zur Unterstützung der Mobilität (MOBOT) und (2) einen Assistenzroboter zur Unterstützung von Duschaktivitäten (I-SUPPORT). Manuskript I dokumentiert eine systematische Literaturanalyse des methodischen Vorgehens bisheriger Studien zur Evaluation robotergestützter Rollatoren aus der Nutzerperspektive. Die meisten Studien zeigen erhebliche methodische Mängel, wie unzureichende Stichprobengrößen/-beschreibungen; Teilnehmer nicht repräsentativ für die Nutzergruppe der robotergestützten Rollatoren; keine geeigneten, standardisierten und validierten Assessmentmethoden und/oder keine Inferenzstatistik. Ein generisches methodisches Vorgehen für die Evaluation robotergestützter Rollatoren konnte nicht identifiziert werden. Für die Konzeption und Durchführung zukünftiger Studien zur Evaluation robotergestützter Rollatoren, aber auch anderer AAL-Systeme werden in Manuskript I abschließend Handlungsempfehlungen formuliert. Manuskript II analysiert die Untersuchungsergebnisse der in Manuskript I identifizierten Studien. Es zeigen sich sehr heterogene Ergebnisse hinsichtlich des Mehrwerts der innovativen Assistenzfunktionen von robotergestützten Rollatoren. Im Allgemeinen werden sie jedoch als positiv von den Nutzern wahrgenommen. Die große Heterogenität und methodischen Mängel der Studien schränken die Interpretierbarkeit ihre Untersuchungsergebnisse stark ein. Insgesamt verdeutlicht Manuskript II, dass die Evidenz zur Effektivität und positiven Wahrnehmung robotergestützter Rollatoren aus der Nutzerperspektive noch unzureichend ist. Basierend auf den Erkenntnissen und Handlungsempfehlungen der systematischen Literaturanalysen aus Manuskript I und II wurden die nutzerorientierten Evaluationsstudien des MOBOT-Rollators konzipiert und durchgeführt (Manuskript III-VI). Manuskript III überprüft die Effektivität des in den MOBOT-Rollator integrierten Navigationssystems bei potentiellen Nutzern (= ältere Personen mit Gangstörungen bzw. Rollator als Gehhilfe im Alltag). Es liefert erstmals einen statistischen Nachweis dafür, dass eine solche Assistenzfunktion effektiv ist, um die Navigationsleistung der Nutzer (z. B. geringer Stoppzeit, kürzere Wegstrecke) – insbesondere derjenigen mit kognitiven Einschränkungen – in einem realitätsnahen Anwendungsszenario zu verbessern. Manuskript IV untersucht die konkurrente Validität des MOBOT-integrierten Ganganalysesystems bei potentiellen Nutzern. Im Vergleich zu einem etablierten Referenzstandard (GAITRite®-System) zeigt es eine hohe konkurrente Validität für die Erfassung zeitlicher, nicht jedoch raumbezogener Gangparameter. Diese können zwar ebenfalls mit hoher Konsistenz gemessen werden, aber lediglich mit einer begrenzten absoluten Genauigkeit. Manuskript V umfasst die nutzerorientierte Evaluation der im MOBOT-Rollator integrierten Assistenzfunktion zur Hindernisvermeidung und belegt erstmals die Effektivität einer solchen Funktionen bei potentiellen Nutzern. Unter Verwendung des für den MOBOT-Rollator neu entwickelten technischen Ansatzes für die Hindernisvermeidung zeigten die Teilnehmer signifikante Verbesserungen bei der Bewältigung eines Hindernisparcours (weniger Kollisionen und geringere Annäherungsgeschwindigkeit an die Hindernisse). Manuskript VI dokumentiert die Effektivität und Zufriedenheit mit der Aufstehhilfe des MOBOT-Rollators von potentiellen Nutzern. Es wird gezeigt, dass die Erfolgsrate für den Sitzen-Stehen-Transfer älterer Personen mit motorischen Einschränkungen durch die Aufstehhilfe signifikant verbessert werden kann. Die Ergebnisse belegen zudem eine hohe Nutzerzufriedenheit mit dieser Assistenzfunktion, insbesondere bei Personen mit höherem Body-Mass-Index. Manuskript VII untersucht die Mensch-Roboter-Interaktion zwischen dem I-SUPPORT-Duschroboter und seiner potentiellen Nutzer (= ältere Personen mit Problemen bei Baden/Duschen) und überprüft deren Effektivität sowie Zufriedenheit mit drei unterschiedlich autonomen Betriebsmodi. Die Studienergebnisse dokumentieren, dass sich mit zunehmender Kontrolle des Nutzers (= abnehmende Autonomie des Duschroboters) nicht nur die Effektivität für das Abduschen eines definierten Körperbereichs verringert, sondern auch die Nutzerzufriedenheit sinkt. Manuskript VIII umfasst die Evaluation eines spezifischen Nutzertrainings auf die gestenbasierte Mensch-Roboter-Interaktion mit dem I-SUPPORT-Duschroboter. Es wird gezeigt, dass ein solches Training die Ausführung der Gesten potentieller Nutzer und sowie die Gestenerkennungsrate des Duschroboters signifikant verbessern, was insgesamt auf eine optimierte Mensch-Roboter-Interaktion in Folge des Trainings schließen lässt. Teilnehmer mit der schlechtesten Ausgangsleistung in der Ausführung der Gesten und mit der größten Angst vor Technologien profitierten am meisten vom Nutzertraining. Insgesamt belegen die Studienergebnisse zur nutzerorientierten Evaluation des MOBOT-Rollators die Effektivität und Gültigkeit seiner innovativen Teilfunktionen. Sie weisen auf ein hohes Potential der Assistenzfunktionen (Navigationssystem, Hindernisvermeidung, Aufstehhilfe) zur Verbesserung der Mobilität älterer Menschen mit motorischen Einschränkungen hin. Vor dem Hintergrund der methodischen Mängel und unzureichenden evidenzbasierten Datenlage hierzu, liefert diese Dissertationsschrift erstmals statistische Belege für den Mehrwert solcher Teilfunktionen bei potentiellen Nutzern und leistet somit einen wichtigen Beitrag zur Schließung der bisherigen Forschungslücke hinsichtlich des nutzerorientierten Wirksamkeits- und Gültigkeitsnachweises robotergestützter Rollatoren und ihrer innovativen Teilfunktionen. Die Ergebnisse der Studien des I-SUPPORT-Duschroboters liefern wichtige Erkenntnisse hinsichtlich der Mensch-Roboter-Interaktion im höheren Alter. Sie zeigen, dass bei älteren Nutzern für eine effektive Interaktion Betriebsmodi mit einem hohen Maß an Autonomie des Duschroboters notwendig sind. Trotz ihrer eingeschränkten Kontrolle über den Roboter, waren die Nutzer mit dem autonomsten Betriebsmodus sogar am zufriedensten. Darüber hinaus unterstreichen die Ergebnisse hinsichtlich der gestenbasierten Interaktion mit dem I-SUPPORT-Duschroboter, dass zukünftige Entwicklungen von altersgerechten Assistenzrobotern mit gestenbasierter Interaktion nicht nur die Verbesserungen technischer Aspekte, sondern auch die Sicherstellung und Verbesserungen der Qualität der Nutzergesten für die Mensch-Roboter-Interaktion durch geeignete Trainings- oder Schulungsmaßnahmen berücksichtigen sollten. Das vorgestellte Nutzertraining könnte hierfür ein mögliches Modell darstellen

    Low obstacles avoidance for lower limb exoskeletons

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    Gli esoscheletri motorizzati per gli arti inferiori (LLEs) sono robot indossabili che permettono a soggetti con disabilità degli arti inferiori di camminare indipendentemente, e persino migliorare le prestazioni degli arti inferiori nel caso di soggetti sani. Nonostante i recenti sviluppi, l'uso di questa promettente tecnologia è relegato agli ambiti clinici e di ricerca; il suo utilizzo come strumento per camminare in modo indipendente in ambienti non controllati è ancora mancante. Il motivo principale di questa limitazione è dovuto alla mancanza di adattabilità degli LLE ai diversi ambienti che possono essere incontrati durante il cammino: la maggioranza degli LLE sfrutta traiettorie predefinite degli arti inferiori senza valutare l'ambiente circostante. Questo implica che ogni tipo di controllo addizionale è a carico dell'utente, e risulta in un sovraccarico fisico e cognitivo da parte di quest'ultimo. Questa tesi si pone l'obbiettivo di superare le limitazioni sopracitate, proponendo un approccio innovativo per aumentare l'autonomia degli LLE. In particolare, il metodo proposto ha lo scopo di stimare la traiettoria degli arti inferiori ottimale, così da poter superare in modo autonomo gli ostacoli bassi che potrebbero essere incontrati lungo il cammino. Tramite l'uso di una stereo camera unita ad un algoritmo di Computer Vision, l'ambiente viene percepito in modo da identificare il pavimento e gli ostacoli che potrebbero influenzare il cammino con l'obbiettivo di selezionare il punto d'appoggio ottimale per il piede. Successivamente, un algoritmo iterativo per la generazione della traiettoria del piede senza collisioni (CFFTG) permette di ottenere i dati necessari a calcolare la cinematica inversa dell'esoscheletro, ed infine gli angoli ai giunti ottenuti da quest'ultima vengono forniti ai controllori dei motori per effettuare il movimento desiderato. Test sperimentali in simulazione (basati su dati reali) sono stati eseguiti per valutare il comportamento dell'algoritmo di Computer Vision e del CFFTG, mostrando ottimi risultati in diversi scenari. Inoltre, le assunzioni su cui si basa la soluzione proposta permettono la sua compatibilità con la maggioranza degli esoscheletri commerciali e di ricerca attualmente disponibili. Credo che pensare agli esoscheletri come degli agenti semi autonomi, piuttosto che come dei dispositivi controllati unicamente dall'utente, rappresenti non solo un percorso che porterà alla simbiosi tra uomo ed esoscheletro, ma anche all'uso di questa tecnologia nella vita di tutti i giorni.Powered lower limb exoskeletons (LLEs) are innovative wearable robots that allow independent walking in people with severe gait impairments, or even to augment lower limb capabilities of able-bodied users. Despite the recent advancements, the use of this promising technology is still restricted to controlled research/clinical settings; uptake in real-life conditions as a device to promote user independence is still lacking. The main reason behind this limitation can be traced back to the lack adaptability of LLEs to the different walking conditions that may be encountered in real world settings: the majority of LLEs relies on predefined gait trajectories and is generally unaware of the environment in which gait occurs. This means that the control burden is entirely on the user, resulting in an increased physical and cognitive workload. This thesis aims at overcoming the aforementioned limitations by proposing a novel approach to enhance the autonomy of the LLEs. In particular, the proposed method has the purpose of estimating the optimal gait trajectory of the exoskeleton in order to autonomously avoid low obstacles on the ground. By using a depth camera coupled with Computer Vision software module, the environment is sensed to detect the ground plane and obstacles that might interfere with the forward motion, in order to predict the following foothold. Then, an iterative-based collision-free foot trajectory generator (CFFTG) algorithm is proposed to calculate the optimal foot motion and the joints’ angles to be sent to the exoskeleton low-level controllers. Experimental tests have been carried out in simulation to evaluate both the CV module and the CFFTG based on real data, showing successful performance in different scenarios. In addition, the assumptions that have been considered in this work make the proposed approach compatible with the majority of exoskeletons in research and on the market. I believe that re-thinking exoskeletons as semi-autonomous agents will represent not only the cornerstone to promote a more symbiotic human-exoskeleton interaction but may also pave the way for the use of this technology in the everyday life

    An Intelligent Ambulatory Fall Risk Assessment Method Based on the Detection of Compensatory Balance Reactions and Environmental Factors

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    Falls in older adults are a critical public health problem worldwide and impact one in three older adults at least once each year. In addition to physical consequences (e.g., hip fracture) falls can lead to negative psychological outcomes, such as depression. Fall risk assessment (FRA) is the initial step for fall prevention programs and interventions. In particular, clinicians aim to understand what factors put older adults at high risk of falling to inform the selection and timing of fall prevention interventions (e.g., strengthening programs). These risk factors are generally categorized as intrinsic or biological (e.g., gait and balance disorders) and extrinsic or environmental (e.g., slippery surfaces). While supervised FRAs, including performance-based (e.g., Timed up and Go) and instrumented methods (e.g., motion capture systems), capable of quantifying intrinsic risks have advanced significantly, falls still remain a major priority in geriatric medicine and public health. This can be due to the Hawthorne effect, the heterogeneous nature of older adults' health, lifestyle, and behaviors, and the complex, multifactorial etiology of falls. To address the limitation of supervised FRAs, a growing body of literature has focused on wearable sensor-based methods for free-living (or ambulatory) FRA. These studies, reviewed in Chapter 2, investigated the relationships between free-living digital biomarkers (FLDBs) extracted from wearable sensors data (generally, inertial data) and the frequency of prospective/retrospective falls in older adults. However, many FLDBs exhibited inconsistent fall predictive powers across studies, indicating they may not be stable in distinguishing fall-prone individuals. Moreover, the relationships between falls and free-living dynamic postural control measures, such as step width and the frequency of naturally-occurring compensatory balance reactions (CBRs), have yet to be investigated in depth. Considering controlled studies reported balance impairment as one of the strongest risk factors for falls, the investigation of balance-related FLDBs may lead to more stable risk assessments and provide new insights into fall prevention in older adults. Although gait-related FLDBs extracted from inertial data can be impacted by both intrinsic and environmental factors, their respective impacts have not been differentiated by the majority of free-living FRA methods. This may lead to the ambiguous interpretation of the subsequent FLDBs, and less precise intervention strategies to prevent falls. A context-aware free-living FRA would elucidate the interplay between intrinsic and environmental risk factors and clarifies their respective impacts on fall predictive powers of FLDBs. This may subsequently enable clinicians to target more specific intervention strategies including environmental modification (e.g., eliminating tripping hazards) and/or rehabilitation interventions (e.g., training to negotiate stairs/transitions). This doctoral thesis aims to address the aforementioned research gaps by proposing multiple machine learning frameworks and incorporating an egocentric camera along with wearable inertial measurement units (IMUs). Chapter 3 discusses the development of random forest models to differentiate between normal gait episodes and multidirectinoal CBRs (e.g, slip-like, trip-like, sidestep) elicited by a perturbation treadmill in controlled conditions in healthy young adults, where the CBR detection model achieved the overall accuracy of ~96%. This chapter established the infrastructure for Chapter 4, where a validation study was performed to detect older adults' CBRs under free-living conditions. Random forest models were trained on independent/unseen datasets curated from multiple sources, including perturbation treadmill CBRs. By investigating 11 fallers' and older non-fallers' free-living criterion standard data, 8 naturally-occurring CBRs, i.e., 7 trips (self-reported using a wrist-mounted voice-recorder) and 1 hit/bump (verified using egocentric vision data) were localized in the corresponding trunk-mounted IMU data. A subset of models differentiated between naturally-occurring CBRs and free-living activities with high sensitivity (100%) and specificity (~99%) suggesting that accurate detection of naturally-occurring CBRs is feasible. Moreover, to address the limitations of IMUs in terms of the estimation of step width in free-living conditions, Chapter 5 presents a novel markerless deep learning-based model to obtain gait patterns by localizing feet in the egocentric vision data captured by a waist-mounted camera. With the aim of improving the interpretability of gait-related FLDBs and investigating the impact of environment on older adults' gait, Chapter 6 proposes a vision-based framework to automatically detect the most common level walking surfaces. Using a belt-mounted camera and IMUs worn by fallers and non-fallers (mean age 73.6 yrs), a unique dataset was acquired (a subset of Multimodal Ambulatory Gait and Fall Risk Assessment in the Wild (MAGFRA-W) dataset). A series of ConvNets were developed: EgoPlaceNet categorizes frames into indoor and outdoor; and EgoTerrainNet (with outdoor and indoor versions) detects the enclosed terrain type in patches. EgoPlaceNet detected outdoor and indoor scenes in MAGFRA-W with 97.36% and 95.59% (leave-one-subject-out) accuracies, respectively. EgoTerrainNet-Indoor and -Outdoor achieved high detection accuracies for pavement (87.63%), foliage (91.24%), gravel (95.12%), and high-friction materials (95.02%), which indicate the models' high generalizabiliy. Overall, promising results encourage the integration of wearable cameras and machine learning approaches to complement IMU-based free-living FRAs, towards stable context-aware FLDBs for fall prevention in older adults. Implications for further research to examine the relationships between naturally-occurring CBRs and fall risk, and clinical applications are discussed

    Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

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    This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

    Design and Development of Biofeedback Stick Technology (BfT) to Improve the Quality of Life of Walking Stick Users

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    Biomedical engineering has seen a rapid growth in recent times, where the aim to facilitate and equip humans with the latest technology has become widespread globally. From high-tech equipment ranging from CT scanners, MRI equipment, and laser treatments, to the design, creation, and implementation of artificial body parts, the field of biomedical engineering has significantly contributed to mankind. Biomedical engineering has facilitated many of the latest developments surrounding human mobility, with advancement in mobility aids improving human movement for people with compromised mobility either caused by an injury or health condition. A review of the literature indicated that mobility aids, especially walking sticks, and appropriate training for their use, are generally prescribed by allied health professionals (AHP) to walking stick users for rehabilitation and activities of daily living (ADL). However, feedback from AHP is limited to the clinical environment, leaving walking stick users vulnerable to falls and injuries due to incorrect usage. Hence, to mitigate the risk of falls and injuries, and to facilitate a routine appraisal of individual patient’s usage, a simple, portable, robust, and reliable tool was developed which provides the walking stick users with real-time feedback upon incorrect usage during their activities of daily living (ADL). This thesis aimed to design and develop a smart walking stick technology: Biofeedback stick technology (BfT). The design incorporates the approach of patient and public involvement (PPI) in the development of BfT to ensure that BfT was developed as per the requirements of walking stick users and AHP recommendations. The newly developed system was tested quantitatively for; validity, reliability, and reproducibility against gold standard equipment such as the 3D motion capture system, force plates, optical measurement system for orientation, weight bearing, and step count. The system was also tested qualitatively for its usability by conducting semi-informal interviews with AHPs and walking stick users. The results of these studies showed that the newly developed system has good accuracy, reported above 95% with a maximum inaccuracy of 1°. The data reported indicates good reproducibility. The angles, weight, and steps recorded by the system during experiments are within the values published in the literature. From these studies, it was concluded that, BfT has the potential to improve the lives of walking stick users and that, with few additional improvements, appropriate approval from relevant regulatory bodies, and robust clinical testing, the technology has a huge potential to carve its way to a commercial market
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