Maximizing Performance with Minimal Resources for Real-Time Transition Detection

Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and enhancing user experience. We here present an approach for real-time transition detection, aimed at optimizing the processing-time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method improves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The efficacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, reinforcing its potential for integration into practical applications.Comment: Submitted for a conference. 7 pages including references, 8 figures, 3 table

System Identification of Bipedal Locomotion in Robots and Humans

The ability to perform a healthy walking gait can be altered in numerous cases due to gait disorder related pathologies. The latter could lead to partial or complete mobility loss, which affects the patients’ quality of life. Wearable exoskeletons and active prosthetics have been considered as a key component to remedy this mobility loss. The control of such devices knows numerous challenges that are yet to be addressed. As opposed to fixed trajectories control, real-time adaptive reference generation control is likely to provide the wearer with more intent control over the powered device. We propose a novel gait pattern generator for the control of such devices, taking advantage of the inter-joint coordination in the human gait. Our proposed method puts the user in the control loop as it maps the motion of healthy limbs to that of the affected one. To design such control strategy, it is critical to understand the dynamics behind bipedal walking. We begin by studying the simple compass gait walker. We examine the well-known Virtual Constraints method of controlling bipedal robots in the image of the compass gait. In addition, we provide both the mechanical and control design of an affordable research platform for bipedal dynamic walking. We then extend the concept of virtual constraints to human locomotion, where we investigate the accuracy of predicting lower limb joints angular position and velocity from the motion of the other limbs. Data from nine healthy subjects performing specific locomotion tasks were collected and are made available online. A successful prediction of the hip, knee, and ankle joints was achieved in different scenarios. It was also found that the motion of the cane alone has sufficient information to help predict good trajectories for the lower limb in stairs ascent. Better estimates were obtained using additional information from arm joints. We also explored the prediction of knee and ankle trajectories from the motion of the hip joints

Effects of overground walking with a robotic exoskeleton on lower limb muscle synergies

Les exosquelettes robotisés de marche (ERM) représentent une intervention prometteuse dans le domaine de la réadaptation locomotrice. Sur le plan clinique, les ERM facilitent la mise en application de principes de neuroplasticité. Jusqu'à présent, la majorité des études analysant les effets de l’ERM a été menée avec des ERM fournissant une assistance robotique complète le long d’une trajectoire de mouvements prédéfinie des membres inférieurs (MI) de façon à reproduire la marche de façon quasi parfaite à très basse vitesse. La nouvelle génération d’ERM, maintenant disponible sur le marché, propose de nouveaux modes de contrôles qui permettent, entre autres, une liberté de mouvement accrue aux MIs (c.-à-d. trajectoire non imposée) et une possibilité d’offrir une assistance ou résistance aux mouvements de différentes intensités surtout pendant la phase d’oscillation du cycle de marche. Cependant, les effets de ces modes de contrôles sur la coordination musculaire des MI pendant la marche au sol avec l’ERM, caractérisé via l’extraction de synergies musculaires (SM), restent méconnus. Cette thèse mesure et compare les caractéristiques des SM (c.-à-d. nombre, profils d’activation, composition musculaire et contribution relative des muscles) pendant la la marche au sol sans ou avec un ERM paramétré avec six différents modes de contrôle chez des individus en bonne santé (articles #1 et #2) et d’autres ayant une lésion médullaire incomplète (LMI) (article #3). Les signaux électromyographiques (EMG) des différents muscles clés des MI, enregistrés lors de la marche, ont été utilisés afin d’extraire les SM avec un algorithme de factorisation matricielle non négative. La similarité des cosinus et les coefficients de corrélation ont caractérisé les similitudes entre les caractéristiques des SM. Les résultats montrent que: 1) les profils d'activation temporelle et le nombre de SM sont modifiés en fonction de la vitesse de marche avec, entre autres une augmentation de la vitesse de marche entrainant une fusion de SM, chez les individus en bonne santé marchant sans ERM ; 2) lorsque ces derniers marchent avec un ERM, les différents modes de contrôle testés ne dupliquent pas adéquatement les SM retrouvées lors de la marche sans ERM. En fait, uniquement le mode de contrôle libérant la contrainte de trajectoire de mouvements des MIs dans le plan sagittal lors de la phase d’oscillation reproduit les principales caractéristiques des SM retrouvées pendant la marche sans ERM ; 3) le nombre et la composition musculaire des SM sont modifiés pendant la marche sans ERM chez les personnes ayant une LMI. Cependant, parmi tous les modes de contrôle étudiés, seul le mode de contrôle libérant le contrôle de la trajectoire de mouvements des MI et assistant l’oscillation du MIs (c.-à-d. HASSIST) permets l’extraction de SM similaire à celles observées chez des individus en santé lors d'une marche sans ERM. Dans l’ensemble, cette thèse a mis en évidence le fait que différentes demandes biomécaniques liées à la marche (c.-à-d. vitesse de marche, modes de contrôle de l’ERM) modifient le nombre et les caractéristiques de SM chez les personnes en santé. Cette thèse a également confirmé que la coordination musculaire, mise en évidence via l’analyse de SM, est altérée chez les personnes ayant une LMI et a tendance à se normaliser lors de la marche avec l’ERM paramétré dans le mode de HASSIST. Les nouvelles preuves appuieront les professionnels de la réadaptation dans le processus de prise de décision concernant la sélection du mode de contrôle des MIs lors de l’entrainement locomoteur utilisant avec un ERM.Wearable robotic exoskeletons (WRE) represent a promising rehabilitation intervention for locomotor rehabilitation training that aligns with activity-based neuroplasticity principles in terms of optimal sensory input, massed repetition, and proper kinematics. Thus far, most studies that investigated the effects of WRE have used WRE that provide full robotic assistance and fixed trajectory guidance to the lower extremity (L/E) to generate close-to-normal walking kinematics, usually at very slow speeds. Based on clinicians’ feedback, current commercially-available WRE have additional control options to be able to integrate these devices into the recovery process of individuals who have maintained some ability to walk after an injury to the central nervous system. In this context, WRE now offer additional degrees of movements for the L/E to move freely and different strategies to assist or resist movement, particularly during the gait cycle’s swing phase. However, the extent that these additional WRE control options affect L/E neuromuscular control during walking, typically characterized using muscle synergies (MSs), remains unknown. This thesis measures and compares MSs characteristics (i.e., number, temporal activation profile, and muscles contributing to a specific synergy [weightings]) during typical overground walking, with and without a WRE, in six different control modes, in abled-bodied individuals (Articles #1 and #2) and individuals with incomplete spinal cord injury (iSCI; Article #3). Surface EMG of key L/E muscles were recorded while walking and used to extract MSs using a non-negative matrix factorization algorithm. Cosine similarity and correlation coefficients characterized, grouped, and indicated similarities between MS characteristics. Results demonstrated that: 1) the number of MSs and MS temporal activation profiles in able-bodied individuals walking without WRE are modified by walking speed and that, as speed increased, specific MSs were fused or merged compared to MSs at slow speeds; 2) In able-bodied individuals walking with WRE, few WRE control modes maintained the typical MSs characteristics that were found during overground walking without WRE. Moreover, freeing the L/E swing trajectory imposed by the WRE best reproduced those MSs characteristics during overground walking without the WRE; and 3) After an iSCI, alterations to the number and the composition of MSs were observed during walking without WRE. However, of all WRE control modes that were investigated, only HASSIST (i.e., freeing WRE control over L/E swing trajectory while assisting the user’s self-selected trajectory) reproduced the number and composition of MSs found in abled-bodied individuals during overground walking without WRE. Altogether, the results of this thesis demonstrated that different walking-related biomechanical demands (i.e., walking speed) and most of the WRE control modes can alter some MSs, and their characteristics, in able-bodied individuals. This research also confirmed that impaired muscle coordination, assessed via MSs, can adapt when walking with a WRE set with specific control options (e.g., HASSIST). These MS adaptations mimicked typical MS characteristics extracted during overground walking. The evidence generated by this thesis will support the decision-making process when selecting specific L/E control options during WRE walking, allowing rehabilitation professionals to refine WRE locomotor training protocols

Human Activity Recognition and Control of Wearable Robots

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/ThesisDoctoral Dissertation Aerospace Engineering 201

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices