Human-Robot Interaction Strategies for Walker-Assisted Locomotion

Introducción:El entrenamiento de la marcha mediante dispositivos robóticos en pacientes con ictus constituye una modalidad de tratamiento ampliamente investigada. Por tanto, existe mucha información heterogénea que es necesario sintetizar, ordenar y clasificar. Por ello, el objetivo de este trabajo es sintetizar y analizar la evidencia científica sobre la aplicación de dispositivos robóticos para el entrenamiento de la marcha en personas con ictus.Métodos:Esta revisión de revisiones sistemáticas y metaanálisis se llevó a cabo siguiendo las recomendaciones de Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Se realizaron búsquedas en 4 bases de datos electrónicas: PubMed, Scopus, Web of Science y Cochrane Library Plus. Se incluyeron revisiones sistemáticas y metaanálisis que incluyeran ensayos controlados aleatorizados (ECA) que investigasen los efectos de los dispositivos robóticos en combinación o no con otro tratamiento de fisioterapia en la recuperación de la marcha en pacientes con ictus.Resultados:Se incluyeron 13 estudios con un total de 101 ECA. Se extrajeron los datos relativos a la población, medidas de resultados, los protocolos de entrenamiento y los resultados principales. Se aplicó la escala A Messurement Tool to Assess Systematic Review (AMSTAR-2) y el Sistema Grading of Recommendations Assessment, Development, and Evaluation (GRADE) de certeza de la evidencia. Solo un estudio tuvo una certeza de evidencia alta; mientras que, cuatro obtuvieron una certeza moderada, seis se clasificaron con una certeza baja y dos obtuvieron una calidad críticamente baja.Conclusiones:El entrenamiento robótico combinado con fisioterapia mejora la velocidad de marcha tras un ictus, especialmente con efectores finales. Sin embargo, los beneficios no alcanzan límites funcionales clínicamente significativos y su aplicabilidad es limitada por la evidencia, el coste y la accesibilidad.El número de registro de PROSPERO:CRD42021237915, https://www.crd.york.ac.uk/PROSPERO/view/CRD42021237915.

The main objective of this research project is to develop a low cost computerized system to automatically diagnose gait disorders and characterize their severity. The system uses 2 video cameras to provide a 3D position acquisition system connected to a personal computer. The patient gait and posture are analyzed from the data acquired by a vision-based gait acquisition system. The whole system will be an important novel tool in medical rehabilitation and diagnosis, resulting on a more effective functional rehabilitation of a patient's gait, assessing their clinical evolution and solving the limitations of the current subjective gait diagnosis tools. The system allows the calculation of 17 human gait joint trajectories. This system will provide a much more objective understanding of the patient's clinical evolution, and thus enables a more effective functional rehabilitation of a patient's gait. In this paper it is presented the selection of the relevant diagnosis parameters of the gait patterns, which is one of the steps to get to the main objective, the automatic diagnosis of human gaits.

Robotic gait rehabilitation devices enable efficient and convenient gait rehabilitation by mimicking the functions of physical theraphists. In manual gait rehabilitation training, physical theraphists have patients practice and memorize normal gait patterns by applying assistive torque to the patient’s joint once the patient’s gait deviates from the normal gait. Thus, one of the most important factors in robotic gait rehabilitation devices is to determine the assistive torque to the patient’s joint during rehabilitation training. In order to calculate the assistive torque, the desired gait trajectory for affected leg should be determined. This reference trajectory is obtained from his healthy leg. The obtained signal has many noisy factors. Therefore, after conditioning the signal, the suitable pattern is applied to the developed system. This procedure is called ”gait pattern generation” in this paper. It is described and discussed in detail.

This paper presents an indirect user interface solution for an active walking aid using visual image sensing. The aim of the design is to track the movement of the human feet in order to obtain command signals for driving an active walker to be seen as a robotic rollator. The human gait becomes the input device by which the user controls the actuators of the robotic rollator. Computer vision techniques are used to track the user's feet as end effectors during human gait. A camera is mounted on the frame of the rollator and captures video images of the user's feet. The kinematics of the feet are measured in two dimensions to obtain the position and orientation of the user's body. The measured motion parameters of the feet are relayed to the desired position and orientation of the rollator. The robotic rollator is to be used as a rehabilitation tool during everyday walking and gait training.

Spatiotemporal gait characteristic changes with gait training using the hybrid assistive limb for chronic stroke patients

Clinical effects of robot-assisted gait training and treadmill training for Parkinson's disease. A randomized controlled trial.

This paper discusses the development of Virtual Control Interface (VCI), a Virtual Reality (VR) system for simulating and exploring multimodal context-sensitive interactions between Augmented Reality (AR) glasses and Internet of Things (IoT) devices.Current trends in controlling IoT devices focus on using existing control interfaces such as smartphones. Parallel technological trends indicate that AR glasses are likely to become the next consumer terminal. Therefore, there is a need to explore a new paradigm for IoT control interfaces that leverages the benefits of AR.Towards developing future-proof AR-IoT interfaces, we observed the HCI community’s challenges in using existing hardware to explore the full potential and the limitations of future AR-IoT interfaces. That became the foundation for developing VCI.This paper provides an overview of the advantages and shortcomings of developing such interfaces on smartphones, state-of-the-art AR/Mixed Reality (MR) glasses, and our proposed VR simulated AR environment. This overview outlines how our proposed system could be a superior choice for researchers to investigate, challenge assumptions, prototype, develop, and experimentally test AR-IoT interactions and interfaces with currently available technologies.Furthermore, we present the three pillars we followed to guide the design and development of VCI to make it resemble an actual AR-IoT framework as much as possible while abstracting parts that have little to no effect on AR-IoT HCI research. These pillars are: design transferable architecture, use viable multimodal interactions and explore user interfaces in real-life scenarios.These three principles informed the VCI’s system architecture built upon three main subsystems: handling multi-modal user input, IoT device emulation and connectivity, and managing the AR user interface (UI).Finally, we provide a heuristic evaluation and future work for VCI.KeywordsSmart homeInternet of ThingsAugmented realityVirtual realityAR-IoTMultimodal interactionVirtual Control Interface

The investigation of the neural correlates of human gait, as measured by means of non-invasive electroencephalography (EEG), is of central importance for the understanding of human gait and for novel developments in gait rehabilitation. Particularly, gait-event-related brain potentials (gERPs) may provide information about the functional role of cortical brain regions in human gait control. The purpose of this paper is to explore possible experimental and technical solutions for time-sensitive analysis of human gait ERPs during spontaneous and instructed treadmill walking. A solution (hardware/software) for synchronous recording of gait and EEG data was developed, tested and piloted. The solution consists of a custom-made USB synchronization interface, a time-synchronization module, and a data-merging module, allowing the temporal synchronization of recording devices, time-sensitive extraction of gait markers for the analysis of gERPs, and the training of artificial neural networks. In the present manuscript, the hardware and software components were tested with the following devices: A treadmill with an integrated pressure plate for gait analysis (zebris FDM-T) and an Acticap non-wireless 32-channel EEG system (Brain Products GmbH). The usability and validity of the developed solution was investigated in a pilot study (n = 3 healthy participants, n = 3 females, mean age = 22.75 years). The recorded continuous EEG data were segmented into epochs according to the detected gait markers for the analysis of gERPs. Finally, the EEG epochs were used to train a deep learning artificial neural network as classifier of gait phases. The results obtained in this pilot study, although preliminary, support the feasibility of the solution for the application of gait-related EEG analysis.

Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects

The central nervous system (CNS) control of human gait is complex, including descending cortical control, affective ascending neural pathways, interhemispheric communication, whole brain networks of functional connectivity, and neural interactions between the brain and spinal cord. Many important studies were conducted in the past, which administered gait training using externally targeted methods such as treadmill, weight support, over-ground gait coordination training, functional electrical stimulation, bracing, and walking aids. Though the phenomenon of CNS activity-dependent plasticity has served as a basis for more recently developed gait training methods, neurorehabilitation gait training has yet to be precisely focused and quantified according to the CNS source of gait control. Therefore, we offer the following hypotheses to the field: Hypothesis 1. Gait neurorehabilitation after stroke will move forward in important ways if research studies include brain structural and functional characteristics as measures of response to treatment. Hypothesis 2. Individuals with persistent gait dyscoordination after stroke will achieve greater recovery in response to interventions that incorporate the current and emerging knowledge of CNS function by directly engaging CNS plasticity and pairing it with peripherally directed, plasticity-based motor learning interventions. These hypotheses are justified by the increase in the study of neural control of motor function, with emerging research beginning to elucidate neural factors that drive recovery. Some are developing new measures of brain function. A number of groups have developed and are sharing sophisticated, curated databases containing brain images and brain signal data, as well as other types of measures and signal processing methods for data analysis. It will be to the great advantage of stroke survivors if the results of the current state-of-the-art and emerging neural function research can be applied to the development of new gait training interventions.

Gait rehabilitation is often focused on the legs and overlooks the role of the upper limbs. However, a variety of studies have demonstrated the importance of proper arm swing both during healthy walking and during rehabilitation. In this paper, we describe a method for generating proper arm-swing trajectories in real time using only measurements of the angular velocity of a person's thighs, to be used during gait rehabilitation with self-selected walking speed. A data-driven linear time-invariant transfer function is developed, using frequency-response methods, which captures the frequency-dependent magnitude and phase relationship between the thighs' angular velocities and the arm angles (measured at the shoulder, in the sagittal plane), using a data set of 30 healthy adult subjects. We show that the proposed method generates smooth trajectories for both healthy individuals and patients with mild to moderate Parkinson disease. The proposed method can be used in future robotic devices that integrate arm swing in gait rehabilitation of patients with walking impairments to improve the efficacy of their rehabilitation.

Portable smart devices are getting more and more popular. Many people prefer smart solutions over old devices or pen-and-paper. But developing applications for mobile devices is a challenging task. First, it is difficult to create an intuitive, easy to use interface; second as current mobile platforms are not compatible, the application must be rewritten on all of them. Furthermore it is not trivial to design multimodal interfaces (including speech and graphical I/O) which really improve software usability. For these reasons our goal is to create an XML based scalable, cross platform, multimodal user interface description format and the corresponding interpreter software for different platforms. This technology makes development for mobile devices much faster and easier.

The work presents a newly developed safety concept for application of robotic walking simulators based on the principle of programmable footplates in gait rehabilitation. Unlike robotic hand devices or exoskeleton robots for gait training on treadmills, which can be built relatively lightweight and require only small drives which can hardly do harm to the patient, a programmable footplate walking simulator with permanent foot fixture essentially needs to have powerful drives in order to carry and move the full body weight of the patient. The developed safety concept comprises several redundant algorithms and devices in the real-time robot control software, electrical emergency stop circuitry and machine mechanics. The mechanical core is a machine design offering maximum passive security by covering all moving parts (i.e. robot drives and linkages) and a newly developed foot safety release binding, which is mounted on each footplate. The release binding allows a safe release from the footplate in all directions in any degree of freedom in the sagittal plane. It is combined with an ankle goniometer which is equipped with adjustable emergency stop limit switches, thus ensuring that the allowed ankle range-of-motion is not exceeded.

Aging greatly degrades the balance and locomotion ability of the elderly due to weakened physiology and pathological conditions. This demands balance and fall intervention technologies that help the affected population to regain mobility, independence, and quality of life. A gait rehabilitation and assistive robot, named Mobile Robotic Balance Assistant (MRBA), is developed to assist people with balance impairment to perform activities of daily living (ADLs) in home and community environments. This student's work is to develop the balance and gait evaluation algorithms for the robot. The process of human falls and loss of balance (LoB) is examined. Human instability is described as states of fall, in which the progression differs according to the severity of the instability and the recovery capability of the falling person. The Inverted Pendulum model is examined to find the theoretical indicators for the LoB. An experiment that induces real falls on human subjects is conducted to examine how the LoB evolves in reality. The results indicate that the ground reaction forces, the subjects' kinematics, and the relationship between the Center of Mass (CoM) and the Base of Support (BoS) are effective for instability detection. The findings establish the requirements of the robot, which is thus equipped with the necessary sensors. A foot tracking algorithm is built to locate the lower limbs in 3D space using modeling and optimization in real-time. It allows the BoS to be constructed from the feet position and computes the gait parameters. Comparing with the motion capture system, the tracking errors are less than 20 degrees and 35mm for rotational and translational errors, respectively, for 0.4m/s gait speed; the spatial and temporal gait parameters are computed with errors less than 35mm and 80ms, respectively. Various instability detection algorithms are developed and examined. Two main approaches are taken, namely thresholding on a single feature and predicting the user's future state. The first method compares a single feature with a well-defined threshold such that if the data exceeds the threshold, it is determined to be an LoB. The second method is based on anomaly detection with neural networks to learn the patterns of ADLs for it to predict future data. If the prediction differs significantly from the actual data, an anomaly, hence an LoB, is flagged. The learning-based programs have better performance due to their ability to consider multiple features in both spatial and temporal domains. The best models rely on forces, CoM and feet position data. They yield a low rate of false alarms and can detect 54 - 100% of ADLs with high risk and LoB within 1s. The detection latency is compared with the average timing users recover from a fall, showing that the system allows the users to practice their balance recovery reflex while ensuring their safety. While the work reveals promising results in the development of a balance and gait evaluation algorithm, more work needs to be accomplished for the functionality to mature sufficiently for the intended use. Elderly and patient's data have to be collected to improve the system. The timeliness and accuracy of the algorithms need to be enhanced to cater to more working scenarios. More intelligent solutions for user performance evaluation and human-robot compliance are required to increase its usability.

[Purpose] This study aims to investigate the effects of robotic exoskeleton-assisted gait training on a pediatric patient with peripheral polyneuropathy. [Participant and Methods] A 10-year-old boy with lower extremity weakness attributed to peripheral polyneuropathy underwent a two-week program comprising 10 rehabilitation sessions of powered robotic exoskeleton-assisted gait training (REGT). He was evaluated before and after treatment using the 10-meter walk test, 6-minute walk test, Berg Balance Scale, the Timed Up and Go Test, the Functional Reach Test, the Modified Functional Reach Test, hip and knee flexion/extension angles, and cardiopulmonary exercise testing. [Results] The patient demonstrated improved gait speed, balance, joint mobility, cadence, the maximum oxygen consumption and metabolic equivalents after the REGT. [Conclusion] Robotic exoskeleton devices could provide additional benefits to pediatric patients with peripheral polyneuropathy, pending larger studies to confirm the significance of treatment.