Human activity recognition based on wearable sensors’ data is quite an attractive subject due to its wide application in the fields of healthcare, wellbeing and smart environments. This research is also focussed on predictive performance comparison of machine learning algorithms for activity recognition from wearable sensors’ (MHEALTH) data while employing a comprehensive process. The framework is adapted from well-laid data science practices which addressed the data analyses requirements quite successfully. Moreover, an Analysis Tool is also developed to support this work and to make it repeatable for further work. A detailed comparative analysis is presented for five multi-class classifier algorithms on MHEALTH dataset namely, Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Random Forests (RF). Beside using original MHEALTH data as input, reduced dimensionality subsets and reduced features subsets were also analysed. The comparison is made on overall accuracies, class-wise sensitivity and specificity of each algorithm, class-wise detection rate and detection prevalence in comparison to prevalence of each class, positive and negative predictive values etc. The resultant statistics have also been compared through visualizations for ease of understanding and inference. All five ML algorithms were applied for classification using the three sets of input data. Out of all five, three performed exceptionally well (SVM, KNN, RF) where RF was best with an overall accuracy of 99.9%. Although CART did not perform well as a classification algorithm, however, using it for ranking inputs was a better way of feature selection. The significant sensors using CART ranking were found to be accelerometers and gyroscopes; also confirmed through application of predictive ML algorithms. In dimensionality reduction, the subset data based on CART-selected features yielded better classification than the subset obtained from PCA technique.

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Trip-induced falls are a leading cause of injuries among adults aged 65 years or older. Perturbation-based balance training (PBT) has emerged as an exercise-based fall prevention intervention and shown efficacy in reducing the risk of trip-induced falls. The broad goal of my PhD research was to advance the application of this so-called trip training through three studies designed to address existing knowledge gaps. First, trip training is commonly conducted with the aid of costly specialized treadmills to induce trip-like perturbations. An alternative version of trip training that eliminates the need for a treadmill would enhance training feasibility and enable wider adoption. The goal of the first study was to compare the effects of non-treadmill training (NT), treadmill training (TT), and a control (i.e., no training) on reactive balance after laboratory-induced trips among community-dwelling older adults. After three weeks of the assigned intervention, participants were exposed to two laboratory-induced trips while walking. Results showed different beneficial effects of NT and TT. For example, NT may be more beneficial in improving recovery step kinematics, while TT may be more beneficial in improving trunk kinematics, compared to the control. While the first study showed the effects of PBT on laboratory-induced trips, little is known about how such training affects responses to real-world trips. Responses to real-world trips may be captured using wearable inertial measurement units (IMUs), yet IMUs have not been adequately validated for this use. Therefore, the goal of the second study was to investigate the concurrent validity of IMU-based trunk kinematics against the gold standard optical motion capture (OMC)-based trunk kinematics after overground trips among community-dwelling older adults. During two laboratory-induced trips, participants wore two IMUs placed on the sternum and shoulder, and OMC markers placed at anatomical landmarks of the trunk segment. Results showed that IMU-based trunk kinematics differed between falls and recoveries after overground trips, and exhibited at least good correlation (Pearson's correlation coefficient, r > 0.5) with the gold standard OMC-based trunk kinematics. The goal of the third study was then to explore differences in tripping kinematics between the laboratory and real world using wearable IMUs among community-dwelling older adults. Participants were asked to wear three IMUs (for sternum and both feet) and a voice recorder to capture their responses to real-world losses of balance (LOBs) during their daily activities for three weeks. Results showed a higher variance in laboratory-induced trips than real-world trips, and the study demonstrated the feasibility of using IMUs and a voice recorder to understand the underlying mechanisms and context of real-world LOBs. Overall, this work was innovative by evaluating a non-treadmill version of trip training, establishing the validity of IMUs in capturing kinematic responses after overground trips, and applying IMUs and a voice recorder to assess tripping kinematics in the real world. The results from this work will advance the use of PBT to reduce the prevalence of trip-induced falls and to investigate the real-world effects of such trip training in future studies. / Doctor of Philosophy / Trips and falls are a major health problem especially among older adults who are aged 65 years or older. Researchers have developed an innovative exercise-based fall prevention training program, which has shown to be helpful in reducing trips and falls. The broad goal of my PhD research was to advance the use of this so-called trip training through three new research studies. First, specialized treadmills are commonly used for trip training to simulate trip-induced falls. An alternative version of trip training without a treadmill would allow more people to receive benefits from this training. The goal of the first study was to compare the effects of non-treadmill training (NT), treadmill training (TT), and no training on balance recovery after tripping in the laboratory. Older adults living in the local community were recruited as research participants and completed NT, TT, or no training over three weeks. After that, they attended a laboratory session where they were tripped twice while walking on a walkway. Results showed that NT helped to take a longer and faster recovery step, while TT helped to limit trunk forward bending during tripping, both of which are important movements to prevent falling after tripping. While the first study showed benefits of trip training in the laboratory, not much is known about the benefits of trip training in the real world. Wearable sensors called inertial measurement units can record body movements without laboratory motion capture cameras, but their ability to record dynamic body movements during tripping needs to be tested. The goal of the second study was to evaluate the capabilities of these wearable sensors on recording trunk movements during tripping and compare them to those recorded by laboratory motion capture cameras. Participants were tripped twice in the laboratory, and their trunk movements were recorded by several wearable sensors and laboratory motion capture cameras. Results showed that these wearable sensors can distinguish between fallers and non-fallers after tripping, and that the trunk movements recorded by the wearable sensors were associated with those recorded by the laboratory motion capture cameras. With this confirmation, the third study was designed to compare balance recovery after tripping between the laboratory and real world using wearable sensors. Participants were asked to wear three wearable sensors and a voice recorder during their daily activities for three weeks. The wearable sensors recorded their trunk and feet movements, while the voice recorder was used for participants to provide detailed explanations of balance losses they experienced. Results showed a higher variability in balance recovery from the laboratory trips compared to the real-world trips. In addition, this study demonstrated that wearable sensors and a voice recorder can be used to study how people reacted to a balance loss and what they did to recover (or fall) from it. Overall, my PhD research work suggested a new version of trip training that does not require a treadmill, proved that wearable sensors can be used to record important body movements during tripping, and demonstrated the method to study balance recovery responses in the real world using wearable sensors. The results from the three studies will promote the use of trip training and provide guidelines for evaluating benefits of trip training in the real world.

2025 / Movement is a window into health and disease. Advanced laboratory tools like force plates have significantly advanced movement science; however, their inaccessibility in clinical settings due to cost, time, and expertise prevents clinicians from directly utilizing the measurement, diagnostic, and predictive insights these tools offer. Consequently, current approaches to the neuromotor rehabilitation of highly prevalent neurological conditions – stroke and Parkinson's disease – have limited ability to provide optimal, personalized care. Despite clinicians' desire for personalized, targeted interventions that can enhance patient outcomes, the current movement measurement gap in clinical settings often leads to suboptimal care. Given the heterogeneity of movement impairments in these patient populations, there is a pressing need for clinically accessible movement measurement tools to guide tailored treatments. This dissertation presents the development and evaluation of novel movement assessment algorithms that utilize a minimal set of wearable inertial measurement unit (IMU) sensors to accurately estimate clinically relevant gait metrics in patients with stroke or Parkinson’s disease. Additionally, using a single thigh-mounted IMU, we identified unique movement phenotypes that show promise as diagnostic biomarkers of neurological disease and gait impairment, as well as predictive biomarkers of treatment response. Together, these findings advance the translation of movement science into clinical practice, unlocking the practical use of wearable sensors by clinicians. By facilitating in-clinic estimation of gait metrics and identification of movement phenotypes, this work supports advanced clinical-decision making, including the development of targeted treatment plans and the prediction of recovery outcomes, thereby advancing movement as a true window into health and disease. / 2026-01-29T00:00:00Z

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants. / Dissertation/Thesis / Doctoral Dissertation Aerospace Engineering 2018

Le travail présenté dans ce mémoire porte sur le développement d'un système de surveillance ambulatoire pour la détection de la dyskinésie induite par la Levodopa (LID) chez les patients de la maladie de Parkinson (PD). Le système est composé d’unités de mesure inertielle (IMUs) qui recueillent des signaux de mouvement chez des sujets sains et des patients parkinsoniens. Des méthodes différentes sont évaluées pour la détection de LID avec et sans classification des activités. Les données recueillies auprès des sujets sains sont utilisées pour concevoir un classificateur d'activité fiable. Par la suite, un algorithme qui effectue la classification des activités et la détection de la dyskinésie sur les données recueillies auprès de des patients parkinsoniens est proposé. Une nouvelle approche basée sur l'analyse de réseau complexe est également explorée et présente des résultats intéressants. Les méthodes de traitement développées ont été intégrées dans une plateforme complète d’analyse nommée PARADYSE. / The work presented in this thesis is concerned with the development of an ambulatory monitoring system for the detection of Levodopa Induced Dyskinesia (LID) in Parkinson’s disease (PD) patients. The system is composed of Inertial Measurement Units (IMUs) that collect movement signals from healthy individuals and PD patients. Different methods are evaluated which consist of LID detection with and without activity classification. Data collected from healthy individuals is used to design a reliable activity classifier. Following that, an algorithm that performs activity classification and dyskinesia detection on data collected from PD patients is tested. A new approach based on complex network analysis is also explored and presents interesting results. The evaluated analysis methods are incorporated into a platform PARADYSE in order to further advance the system’s capabilities.

This work explores the blending of e-textile technology with the porous electrode of polymer electrolyte membrane fuel cells (PEMFCs) and with smart wound patches to allow monitoring and in-situ diagnostics. This work includes contributions to understanding water transport and conductivity in the carbon cloth gas diffusion layer (GDL), and further developing thread-based relative humidity (RH) and temperature sensors, which can be sewn on a cloth GDL in PEMFCs. We also explore the application of the developed RH and temperature sensors in wearable biomonitoring. First, an experimental prototype is developed for evaluating water transport, thermal conductivity and electrical conductivity of carbon cloth GDLs under different hydrophobic coatings and compressions. Second, we demonstrate the addition of external threads to the carbon cloth GDL to (1) facilitate water transport and (2) measure local RH and temperature with a minimal impact on the physical, microstructural and transport properties of the GDL. We illustrate the roll-to-roll process for fabricating RH and temperature sensors by dip-coating commodity threads into a carbon nanotubes (CNTs) suspension. The thread-based sensors response to RH and temperature in the working environment of PEMFCs is investigated. As a proof-of-concept, the local temperature of carbon cloth GDL is monitored in an ex-situ experiment. Finally, we optimized the coating parameters (e.g. CNTs concentration, surfactant concentration and a number of dipping) for the thread-based sensors. The response of the thread-based sensors in room conditions is evaluated and shows a linear resistance decrease to temperature and a quadratic resistance increase to RH. We also evaluated the biocompatibility of the sensors by performing cell cytotoxicity and studying wound healing in an animal model. The novel thread-based sensors are not only applicable for textile electrochemical devices but also, show a promising future in wearable biomonitoring applications. / Graduate

The contact patterns among individuals can significantly affect the progress of an infectious outbreak within a population. Gathering data about these interaction and mixing patterns is essential to assess computational modeling of infectious diseases. Various self-report approaches have been designed in different studies to collect data about contact rates and patterns. Recent advances in sensing technology provide researchers with a bilateral automated data collection devices to facilitate contact gathering overcoming the disadvantages of previous approaches. In this study, a novel unilateral wearable sensing architecture has been proposed that overcome the limitations of the bi-lateral sensing. Our unilateral wearable sensing system gather contact data using hybrid sensor arrays embedded in wearable shirt. A smartphone application has been used to transfer the collected sensors data to the cloud and apply deep learning model to estimate the number of human contacts and the results are stored in the cloud database. The deep learning model has been developed on the hand labelled data over multiple experiments. This model has been tested and evaluated, and these results were reported in the study. Sensitivity analysis has been performed to choose the most suitable image resolution and format for the model to estimate contacts and to analyze the model's consumption of computer resources.

Stress and overload are strongly associated with unsafe behaviour, which motivated various studies to detect them automatically in workplaces. This study aims to advance safety research by developing a data-driven stress and overload detection method. An unsupervised deep learning-based anomaly detection method is developed to detect stress. The proposed method performs with convolutional neural network encoder-decoder and long short-term memory equipped with an attention layer. Data from a field experiment with 18 participants was used to train and test the developed method. The field experiment was designed to include a pre-defined sequence of activities triggering mental and physical stress, while a wristband biosensor was used to collect physiological signals. The collected contextual and physiological data were pre-processed and then resampled into correlation matrices of 14 features. Correlation matrices are used as an input to the unsupervised Deep Learning (DL) based anomaly detection method. The developed method is validated, offering accuracy and F-measures close to 0.98. The technique employed captures the input data attributes correlation, promoting higher interpretability of the DL method for easier comprehension. Over-reliance on uncertain absolute truth, the need for a high number of training samples, and the requirement of a threshold for detecting anomalies are identified as shortcomings of the proposed method. To overcome these shortcomings, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed and developed. While the ANFIS method did not improve the overall accuracy, it outperformed the DL-based method in detecting anomalies precisely. The overall performance of the ANFIS method is better than the DL-based method for the anomalous class, and the method results in lower false alarms. However, the DL-based method is suitable for circumstances where false alarms are tolerated. / Dissertation / Doctor of Philosophy (PhD)

Ergonomic assessments to determine risks of work-related musculoskeletal disorders as well as to compare designs of work-tasks and workstations, are imperative for high sustainability and productivity in any given industry. Hence, assessment tools that can objectively and reliably capture postures, joint angles and muscle activities play very important role in properly determining risks relating to work and various tasks. The introduction of direct measurement instruments/tools has been helping and continue to help improve upon observational assessment methods to attain objectivity and reliability. This project aimed at contributing to future improvements of industrial risk assessment measures in ergonomics by identifying and testing direct measurement instruments/tools that can enhance observational risk assessment methods and introduce a new way of signal processing, hence, reducing assessment time while increasing objectivity and reliability. Several candidate instruments were identified and out of the identified, ten were selected as potential candidates. Two out of the ten, Wergonic and ErgoHandMeter were then selected and tested on common observation risk assessment factors that could be measured and answers provided directly or by analyses. The Wergonic instrument was modified to enhance its measuring capability from one fully and partially two factors to six factors. New algorithms were also employed to analyse measurements of the ErgoHandMeter in order to answer questions regarding repetitive movements. The two instruments tested, are able to measure and provide results for six commonly and one rarely assessed biomechanical risk factors. By combining selected potential candidates, many of the commonly targeted biomechanical risk factors in observational instruments can be measured by the selected direct measurement instruments. However, some factors especially force measurement remain a challenge for measuring by direct wearable sensor instruments.