A functional electrical stimulation system for human walking inspired by reflexive control principles

This study presents an innovative multichannel functional electrical stimulation gait-assist system which employs a well-established purely reflexive control algorithm, previously tested in a series of bipedal walking robots. In these robots, ground contact information was used to activate motors in the legs, generating a gait cycle similar to that of humans. Rather than developing a sophisticated closed-loop functional electrical stimulation control strategy for stepping, we have instead utilised our simple reflexive model where muscle activation is induced through transfer functions which translate sensory signals, predominantly ground contact information, into motor actions. The functionality of the functional electrical stimulation system was tested by analysis of the gait function of seven healthy volunteers during functional electrical stimulation–assisted treadmill walking compared to unassisted walking. The results demonstrated that the system was successful in synchronising muscle activation throughout the gait cycle and was able to promote functional hip and ankle movements. Overall, the study demonstrates the potential of human-inspired robotic systems in the design of assistive devices for bipedal walking

Spatio-temporal gait analysis based on human-smart rollator interaction

The ability to walk is typically related to several biomechanical components that are involved in the gait cycle (or stride), including free mobility of joints, particularly in the legs; coordination of muscle activity in terms of timing and intensity; and normal sensory input, such as vision and vestibular system. As people age, they tend to slow their gait speed, and their balance is also affected. Also, the retirement from the working life and the consequent reduction of physical and social activity contribute to the increased incidence of falls in older adults. Moreover, older adults suffer different kinds of cognitive decline, such as dementia or attention problems, which also accentuate gait disorders and its consequences. In this paper we present a methodology for gait identification using the on-board sensors of a smart rollator: the i-Walker. This technique provides the number of steps performed in walking exercises, as well as the time and distance travelled for each stride. It also allows to extract spatio-temporal metrics used in medical gait analysis from the interpretation of the interaction between the individual and the i-Walker. In addition, two metrics to assess users’ driving skills, laterality and directivity, are proposed.Peer ReviewedPostprint (author's final draft

Automatic classification of gait patterns using a smart rollator and the BOSS model

Nowadays, the risk of falling in older adults is a major concern due to the severe consequences it brings to socio-economic and public health systems. Some pathologies cause mobility problems in the aged population, leading them to fall and, thus, reduce their autonomy. Other implications of ageing involve having different gait patterns and walking speed. In this paper, a non-invasive framework is proposed to study gait in elder people using data collected by a smart rollator, the i-Walker. The analysis presented in this article uses a feature extraction method and a spectral embedding to represent the information and Bayesian clustering for the knowledge discovery. The algorithm considers raw data from the i-Walker sensors along with the calculated walking speed of each individual, which has been already used in clinical studies to assess physical and cognitive status of older adults. The results obtained demonstrate that the proposed analysis has the potential to separate in clusters the people of the two groups of interest: young people and geriatric.Peer ReviewedPostprint (author's final draft

First advances in near fall detection and prediction when using a walker

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a major concern to society. Several injuries associated with falls need medical care, and in the worst-case scenario, a fall can lead to death. These consequences have a high cost for the population. In order to overcome these problems, a diversity of approaches for detection, prediction, and prevention of falls have been tackled. Walkers are often prescribed to subjects who present a higher risk of falling. Thus, it is essential to develop strategies to enhance the user's safety in an imminent danger situation. In this sense, this dissertation aims to develop a strategy to detect a near fall (NF) and its direction as well as the detection of incipient near fall (INF) while the subject uses a walker. Furthermore, it has the purpose of detecting two gait events, the heel strike (HS) and the toe-off (TO). The strategies established, in this work, were based on the data gathered through an inertial sensor placed on the lower trunk and force sensors placed on the insoles. Following data collection, the methodology adopted to identify the situations aforementioned was based on machine learning algorithms. In order to reach the model with best performance, many combinations of different classifiers were tested with three feature selection methods. Regarding the detection of NF, the results achieved presented a Matthews Correlation Coefficient (MCC) of 79.99% being possible to detect a NF 1.76±0.76s before its end. With the implementation of the post-processing algorithm, a large part of the false positives was eliminated being able to detect all NF 1.48±0.68s before its end. Concerning the models built to distinguish the direction of the NF, the best model presented accuracy of 58.97% being unable to reliably distinguish the three fall directions. The methodology followed, in this work, was unsuccessful to detect an INF. The best model presented MCC=23.87%, in this case. Lastly, with respect to the detection of HS and TO events the best model reached MCC=86.94%. With the application of the post-processing algorithm, part of misclassified samples was eliminated, however, a delay in the detection of the HS and TO events was introduced. With the post-processing it was possible to reach MCC=88.82%, not including the imposed delay.As quedas representam uma grande preocupação para a sociedade. Várias lesões associadas às quedas necessitam de cuidados médicos e, no pior dos casos, uma queda pode levar à morte. Estas consequências traduzem-se em custos elevados para a população. A fim de ultrapassar estes problemas, várias abordagens têm sido endereçadas para deteção, previsão e prevenção das quedas. Os andarilhos são muitas vezes prescritos a sujeitos que apresentam um risco de queda maior. Desta forma, é essencial desenvolver estratégias para aumentar a segurança do utilizador perante uma situação de perigo iminente. Neste sentido, esta dissertação visa desenvolver uma estratégia que permita a deteção de uma quase queda (NF) e a sua direção, assim como a deteção incipiente de uma NF (INF). Para além disso, tem o objetivo de detetar dois eventos de marcha, o heel strike (HS) e o toe-off (TO). As estratégias definidas, neste trabalho, basearam-se nos dados recolhidos através de um sensor inercial posicionado no tronco inferior e de sensores de força colocados nas palmilhas. Após a aquisição dos dados, a metodologia adotada para identificar as situações anteriormente referidas foi baseada em algoritmos de machine learning. Com o intuito de obter o modelo com melhor desempenho, várias combinações de diferentes classificadores foram testadas com três métodos de seleção de features. No que concerne à deteção da NF, os resultados alcançados apresentaram um Matthews Correlation Coefficient (MCC) de 79.99% sendo possível detetar uma NF 1.76±0.76s antes do seu final. Com a implementação do algoritmo de pós-processamento, grande parte dos falsos positivos foram eliminados, sendo possível detetar todas as NF 1.48±0.68s antes do seu final. Em relação aos modelos construídos para distinguir a direção da NF, o melhor modelo apresentou uma precisão (ACC) de 59.97%. A metodologia seguida neste trabalho não foi bem sucedida na deteção INF. O melhor modelo apresentou um MCC=23.87%. Relativamente à deteção dos eventos, HS e TO, o melhor modelo atingiu um MCC=86.94%. Com a aplicação do algoritmo de pós-processamento parte das amostras mal classificadas foram eliminadas, no entanto, foi introduzido um atraso na deteção do HS e do TO. Com o pós-processamento foi possível obter um MCC=88.82%, não incluindo o atraso imposto pelo pós-processamento

Effects of aging on identifying emotions conveyed by point-light walkers

M.G. was supported by EC FP7 HBP (grant 604102), PITN-GA-011-290011 (ABC) FP7-ICT-2013-10/ 611909 (KOROIBOT), and by GI 305/4-1 and KA 1258/15-1, and BMBF, FKZ: 01GQ1002A. K.S.P. was supported by a BBSRC New Investigator Grant. A.B.S. and P.J.B. were supported by an operating grant (528206) from the Canadian Institutes for Health Research. The authors also thank Donna Waxman for her valuable help in data collection for all experiments described here.Peer reviewedPostprin

A framework for experimental determination of localised vertical pedestrian forces on full-scale structures using wireless attitude and heading reference systems

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.A major weakness among loading models for pedestrians walking on flexible structures proposed in recent years is the various uncorroborated assumptions made in their development. This applies to spatio- temporal characteristics of pedestrian loading and the nature of multi-object interactions. To alleviate this problem, a framework for the determination of localised pedestrian forces on full-scale structures is presented using a wireless attitude and heading reference systems (AHRS). An AHRS comprises a triad of tri-axial accelerometers, gyroscopes and magnetometers managed by a dedicated data processing unit, allowing motion in three-dimensional space to be reconstructed. A pedestrian loading model based on a single point inertial measurement from an AHRS is derived and shown to perform well against benchmark data collected on an instrumented treadmill. Unlike other models, the current model does not take any predefined form nor does it require any extrapolations as to the timing and amplitude of pedestrian loading. In order to assess correctly the influence of the moving pedestrian on behaviour of a structure, an algorithm for tracking the point of application of pedestrian force is developed based on data from a single AHRS attached to a foot. A set of controlled walking tests with a single pedestrian is conducted on a real footbridge for validation purposes. A remarkably good match between the measured and simulated bridge response is found, indeed confirming applicability of the proposed framework.The research presented here was funded by EPSRC (grant EP/I029567/2). Authors thank Devon County Council for permitting the experimental campaign to be conducted on Baker Bridge in Exeter, UK, and Dr Erfan Shahabpour (supported by EPSRC grant EP/K03877X/1) for providing access to and assisting with measurements on the ADAL-3D treadmill at the University of Sheffield (funded by EPSRC grant EP/E018734/1)

An Analysis of the Relationship Between Complexity and Gait Adaptability

The presented sequence of studies considers theoretical applications from Complexity Science and Chaos Theory for gait time-series analysis. The main goal of this research is to build on insights from a previous body of knowledge, which have identified measures derived from Complexity Science and Chaos Theory as critical markers of gait control. Specifically, the studies presented in this dissertation attempt to directly test whether characterizing gait complexity relates to an ability to flexibly adjust gait. The broader impact of this research is utilizing measures of complexity to characterize gait control, and as a tool for rehabilitation which have both gained momentum in fall prevention research. Through a series of four studies, this dissertation was designed to test the theoretical viewpoint that complexity is related to gait control, particularly gait adaptability. Firstly, I sought to develop a paradigm for reliably entraining gait complexity with the use of several auditory fluctuating timing imperatives which, differed based on specified fractal characteristics. I also sought to quantify the duration of the retention of gait complexity, following entrainment. Thirdly, I assessed whether attentional demands required during entrainment were affected by the fractal characteristics of a fluctuating timing imperative. Lastly, I applied the developed paradigm to evaluate the theoretical relationship between gait complexity and stepping performance. The findings from this dissertation have developed a framework for assessing gait control. This series of projects has determined that a fluctuating timing imperative can reliably prescribe the gait pattern of healthy individuals towards a particular complexity. The use of a fluctuating timing imperative leads to entrainment of the stimulus complexity. Furthermore, once the timing imperative has ceased, there is a brief period of complexity retention in the walking pattern. This dissertation has also confirmed that entraining complexity to a fluctuating timing imperative does not alter the attentional demands associated with entrainment. However, entraining gait to fluctuating timing imperatives of different complexities alters the stepping strategy that is adopted. Lastly, this dissertation has shown that synchronizing gait to a fixed-interval stimulus following entrainment, depends on the complexities of the gait pattern

A framework for experimental determination of localised vertical pedestrian forces on full-scale structures using wireless attitude and heading reference systems

A major weakness among loading models for pedestrians walking on flexible structures proposed in recent years is the various uncorroborated assumptions made in their development. This applies to spatio-temporal characteristics of pedestrian loading and the nature of multi-object interactions. To alleviate this problem, a framework for the determination of localised pedestrian forces on full-scale structures is presented using a wireless attitude and heading reference systems (AHRS). An AHRS comprises a triad of tri-axial accelerometers, gyroscopes and magnetometers managed by a dedicated data processing unit, allowing motion in three-dimensional space to be reconstructed. A pedestrian loading model based on a single point inertial measurement from an AHRS is derived and shown to perform well against benchmark data collected on an instrumented treadmill. Unlike other models, the current model does not take any predefined form nor does it require any extrapolations as to the timing and amplitude of pedestrian loading. In order to assess correctly the influence of the moving pedestrian on behaviour of a structure, an algorithm for tracking the point of application of pedestrian force is developed based on data from a single AHRS attached to a foot. A set of controlled walking tests with a single pedestrian is conducted on a real footbridge for validation purposes. A remarkably good match between the measured and simulated bridge response is found, indeed confirming applicability of the proposed framework

Vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance