Human Gait Phase Recognition Based on Thigh Movement Computed using IMUs

Human gait analysis is a major topic in pedestrian navigation and geriatric care. Identifying gait phases is important in using human gait for pedestrian navigation and tracking. Most of existing gait phase identification techniques use multiple sensor modules attached to each section of the lower body. This paper discusses the feasibility of recognizing gait phases using a single inertial measurement unit (IMU) placed in a trouser pocket of the subject. The movement of the thigh is computed by fusing accelerometer and the gyroscopic data gathered from the IMU. Experimental results indicated that most of the major gait phases such as Initial Contact, Load Response, Mid Stance, Terminal Stance, Pre-Swing and Swing can be identified by the movement of one thigh tracked by an IMU. It was also noted that the movement of the offside leg can also be estimated from the fused IMU data. This paper presents a method to recognize all major phases of human stride cycle during walking from movement of one thigh

Analysis of Human Gait Cycle with Body Equilibrium based on leg Orientation

Gait analysis identifies the posture during movement in order to provide the correct actions for a normal gait. A person\u27s gait may differ from others and can be recognized by specific patterns. Healthy individuals exhibit normal gait patterns, while lower limb amputees exhibit abnormal gait patterns. To better understand the pitfalls of gait, it is imperative to develop systems capable of capturing the gait patterns of healthy individuals. The main objective of this research was to introduce a new concept in gait analysis by computing the static and dynamic equilibrium in a real-world environment. A relationship was also presented among the parameters stated as static \& dynamic equilibrium, speed, and body states. A sensing unit was installed on the designed metal-based leg mounting assembly on the lateral side of the leg. An algorithm was proposed based on two variables: the position of the leg in space and the angle of the knee joint measured by an IMU sensor and a rotary encoder. It was acceptable to satisfy the static conditions when the body was in a fixed position and orientation, whether lying down or standing. While walking and running, the orientation is determined by the position and knee angle variables, which fulfill the dynamic condition. High speed reveals a rapid change in orientation, while slow speed reveals a slow change in orientation. The proposed encoder-based feedback system successfully determined the flexion at 47$^\circ$, extension at 153$^\circ$, and all seven gait cycle phases were recognized within this range of motion. Body equilibrium facilitates individuals when they are at risk of falling or slipping

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

Intelligent signal processing for digital healthcare monitoring

Ein gesunder Gang ist ein komplexer Prozess und erfordert ein Gleichgewicht zwischen verschiedenen neurophysiologischen Systemen im Körper und gilt als wesentlicher Indikator für den physischen und kognitiven Gesundheitszustand einer Person. Folglich würden Anwendungen im Bereich der Bioinformatik und des Gesundheitswesens erheblich von den Informationen profitieren, die sich aus einer längeren oder ständigen Überwachung des Gangs, der Gewohnheiten und des Verhaltens von Personen unter ihren natürlichen Lebensbedingungen und bei ihren täglichen Aktivitäten mit Hilfe intelligenter Geräte ergeben. Vergleicht man Trägheitsmess- und stationäre Sensorsysteme, so bieten erstere hervorragende Möglichkeiten für Ganganalyseanwendungen und bieten mehrere Vorteile wie geringe Größe, niedriger Preis, Mobilität und sind leicht in tragbare Systeme zu integrieren. Die zweiten gelten als der Goldstandard, sind aber teuer und für Messungen im Freien ungeeignet. Diese Arbeit konzentriert sich auf die Verbesserung der Zeit und Qualität der Gangrehabilitation nach einer Operation unter Verwendung von Inertialmessgeräten, indem sie eine neuartige Metrik zur objektiven Bewertung des Fortschritts der Gangrehabilitation in realen Umgebungen liefert und die Anzahl der verwendeten Sensoren für praktische, reale Szenarien reduziert. Daher wurden die experimentellen Messungen für eine solche Analyse in einer stark kontrollierten Umgebung durchgeführt, um die Datenqualität zu gewährleisten. In dieser Arbeit wird eine neue Gangmetrik vorgestellt, die den Rehabilitationsfortschritt anhand kinematischer Gangdaten von Aktivitäten in Innen- und Außenbereichen quantifiziert und verfolgt. In dieser Arbeit wird untersucht, wie Signalverarbeitung und maschinelles Lernen formuliert und genutzt werden können, um robuste Methoden zur Bewältigung von Herausforderungen im realen Leben zu entwickeln. Es wird gezeigt, dass der vorgeschlagene Ansatz personalisiert werden kann, um den Fortschritt der Gangrehabilitation zu verfolgen. Ein weiteres Thema dieser Arbeit ist die erfolgreiche Anwendung von Methoden des maschinellen Lernens auf die Ganganalyse aufgrund der großen Datenmenge, die von den tragbaren Sensorsystemen erzeugt wird. In dieser Arbeit wird das neuartige Konzept des ``digitalen Zwillings'' vorgestellt, das die Anzahl der verwendeten Wearable-Sensoren in einem System oder im Falle eines Sensorausfalls reduziert. Die Evaluierung der vorgeschlagenen Metrik mit gesunden Teilnehmern und Patienten unter Verwendung statistischer Signalverarbeitungs- und maschineller Lernmethoden hat gezeigt, dass die Einbeziehung der extrahierten Signalmerkmale in realen Szenarien robust ist, insbesondere für das Szenario mit Rehabilitations-Gehübungen in Innenräumen. Die Methodik wurde auch in einer klinischen Studie evaluiert und lieferte eine gute Leistung bei der Überwachung des Rehabilitationsfortschritts verschiedener Patienten. In dieser Arbeit wird ein Prototyp einer mobilen Anwendung zur objektiven Bewertung des Rehabilitationsfortschritts in realen Umgebungen vorgestellt

IMUs: validation, gait analysis and system’s implementation

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a prevalent problem in actual society. The number of falls has been increasing greatly in the last fifteen years. Some falls result in injuries and the cost associated with their treatment is high. However, this is a complex problem that requires several steps in order to be tackled. Namely, it is crucial to develop strategies that recognize the mode of locomotion, indicating the state of the subject in various situations, namely normal gait, step before fall (pre-fall) and fall situation. Thus, this thesis aims to develop a strategy capable of identifying these situations based on a wearable system that collects information and analyses the human gait. The strategy consists, essentially, in the construction and use of Associative Skill Memories (ASMs) as tools for recognizing the locomotion modes. Consequently, at an early stage, the capabilities of the ASMs for the different modes of locomotion were studied. Then, a classifier was developed based on a set of ASMs. Posteriorly, a neural network classifier based on deep learning was used to classify, in a similar way, the same modes of locomotion. Deep learning is a technique actually widely used in data classification. These classifiers were implemented and compared, providing for a tool with a good accuracy in recognizing the modes of locomotion. In order to implement this strategy, it was previously necessary to carry out extremely important support work. An inertial measurement units’ (IMUs) system was chosen due to its extreme potential to monitor outpatient activities in the home environment. This system, which combines inertial and magnetic sensors and is able to perform the monitoring of gait parameters in real time, was validated and calibrated. Posteriorly, this system was used to collect data from healthy subjects that mimicked Fs. Results have shown that the accuracy of the classifiers was quite acceptable, and the neural networks based classifier presented the best results with 92.71% of accuracy. As future work, it is proposed to apply these strategies in real time in order to avoid the occurrence of falls.As quedas são um problema predominante na sociedade atual. O número de quedas tem aumentado bastante nos últimos quinze anos. Algumas quedas resultam em lesões e o custo associado ao seu tratamento é alto. No entanto, trata-se de um problema complexo que requer várias etapas a serem abordadas. Ou seja, é crucial desenvolver estratégias que reconheçam o modo de locomoção, indicando o estado do sujeito em várias situações, nomeadamente, marcha normal, passo antes da queda (pré-queda) e situação de queda. Assim, esta tese tem como objetivo desenvolver uma estratégia capaz de identificar essas situações com base num sistema wearable que colete informações e analise a marcha humana. A estratégia consiste, essencialmente, na construção e utilização de Associative Skill Memories (ASMs) como ferramenta para reconhecimento dos modos de locomoção. Consequentemente, numa fase inicial, foram estudadas as capacidades das ASMs para os diferentes modos de locomoção. Depois, foi desenvolvido um classificador baseado em ASMs. Posteriormente, um classificador de redes neuronais baseado em deep learning foi utilizado para classificar, de forma semelhante, os mesmos modos de locomoção. Deep learning é uma técnica bastante utilizada em classificação de dados. Estes classificadores foram implementados e comparados, fornecendo a uma ferramenta com uma boa precisão no reconhecimento dos modos de locomoção. Para implementar esta estratégia, era necessário realizar previamente um trabalho de suporte extremamente importante. Um sistema de unidades de medição inercial (IMUs), foi escolhido devido ao seu potencial extremo para monitorizar as atividades ambulatórias no ambiente domiciliar. Este sistema que combina sensores inerciais e magnéticos e é capaz de efetuar a monitorização de parâmetros da marcha em tempo real, foi validado e calibrado. Posteriormente, este Sistema foi usado para adquirir dados da marcha de indivíduos saudáveis que imitiram quedas. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e o classificador baseado em redes neuronais apresentou os melhores resultados com 92.71% de precisão. Como trabalho futuro, propõe-se a aplicação destas estratégias em tempo real de forma a evitar a ocorrência de quedas

Human Gait Modeling, Prediction and Classification for Level Walking Using Harmonic Models Derived from a Single Thigh-Mounted IMU

The majority of human gait modeling is based on hip, foot or thigh acceleration. The regeneration accuracy of these modeling approaches is not very high. This paper presents a harmonic approach to modeling human gait during level walking based on gyroscopic signals for a single thigh-mounted Inertial Measurement Unit (IMU) and the flexion–extension derived from a single thigh-mounted IMU. The thigh angle can be modeled with five significant harmonics, with a regeneration accuracy of over 0.999 correlation and less than 0.5◦ RMSE per stride cycle. Comparable regeneration accuracies can be achieved with nine significant harmonics for the gyro signal. The fundamental frequency of the harmonic model can be estimated using the stride time, with an error level of 0.0479% (±0.0029%). Six commonly observed stride patterns, and harmonic models of thigh angle and gyro signal for those stride patterns, are presented in this paper. These harmonic models can be used to predict or classify the strides of walking trials, and the results are presented herein. Harmonic models may also be used for activity recognition. It has shown that human gait in level walking can be modeled with a harmonic model of thigh angle or gyro signal, using a single thigh-mounted IMU, to higher accuracies than existing techniques

A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons

Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in Scopus and Web of Science databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.This work was funded in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under grant 2020.05711.BD, under the Stimulus of Scientific Employment with the grant 2020.03393.CEECIND, and in part by the FEDER Funds through the COMPETE 2020— Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Wearable inertial sensors for human movement analysis

Introduction: The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.Areas covered: Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice.Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine