Neuromechanical and environment aware machine learning tool for human locomotion intent recognition

Current research suggests the emergent need to recognize and predict locomotion modes (LMs) and LM transitions to allow a natural and smooth response of lower limb active assistive devices such as prostheses and orthosis for daily life locomotion assistance. This Master dissertation proposes an automatic and user-independent recognition and prediction tool based on machine learning methods. Further, it seeks to determine the gait measures that yielded the best performance in recognizing and predicting several human daily performed LMs and respective LM transitions. The machine learning framework was established using a Gaussian support vector machine (SVM) and discriminative features estimated from three wearable sensors, namely, inertial, force and laser sensors. In addition, a neuro-biomechanical model was used to compute joint angles and muscle activations that were fused with the sensor-based features. Results showed that combining biomechanical features from the Xsens with environment-aware features from the laser sensor resulted in the best recognition and prediction of LM (MCC = 0.99 and MCC = 0.95) and LM transitions (MCC = 0.96 and MCC = 0.98). Moreover, the predicted LM transitions were determined with high prediction time since their detection happened one or more steps before the LM transition occurrence. The developed framework has potential to improve the assistance delivered by locomotion assistive devices to achieve a more natural and smooth motion assistance.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, and part by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs -Controlo Inteligente de um Sistema Ortótico Ativo e Autónomo- under Grant NORTE-01-0145-FEDER-030386, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Daily locomotion recognition and prediction: A kinematic data-based machine learning approach

More versatile, user-independent tools for recognizing and predicting locomotion modes (LMs) and LM transitions (LMTs) in natural gaits are still needed. This study tackles these challenges by proposing an automatic, user-independent recognition and prediction tool using easily wearable kinematic motion sensors for innovatively classifying several LMs (walking direction, level-ground walking, ascend and descend stairs, and ascend and descend ramps) and respective LMTs. We compared diverse state-of-the-art feature processing and dimensionality reduction methods and machine-learning classifiers to find an effective tool for recognition and prediction of LMs and LMTs. The comparison included kinematic patterns from 10 able-bodied subjects. The more accurate tools were achieved using min-max scaling [-1; 1] interval and 'mRMR plus forward selection' algorithm for feature normalization and dimensionality reduction, respectively, and Gaussian support vector machine classifier. The developed tool was accurate in the recognition (accuracy >99% and >96%) and prediction (accuracy >99% and >93%) of daily LMs and LMTs, respectively, using exclusively kinematic data. The use of kinematic data yielded an effective recognition and prediction tool, predicting the LMs and LMTs one-step-ahead. This timely prediction is relevant for assistive devices providing personalized assistance in daily scenarios. The kinematic data-based machine learning tool innovatively addresses several LMs and LMTs while allowing the user to self-select the leading limb to perform LMTs, ensuring a natural gait.This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015 and SFRH/BD/147878/2019, by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

VALORACIÓN DEL PARÁMETRO EFICIENCIA SEGÚN LA TÉNICA DE CARRERA ANALIZADA MEDIANTE RUNSCRIBE® (IMU) PROPUESTA DE ESTUDIO

El “running” es un deporte que ha aumentado su práctica en los últimos años. Hoy en día lo practican el 22,6% de las personas que realizan deporte1 mientras que el índice de lesiones se encuentra entre el 19% y el 79%3,4. El desarrollo de lesiones y la carga generada puede afectar a la eficiencia de la técnica de carrera4,12,14,15,16. Por tanto, proponemos una investigación en el que se valora tanto la técnica de carrera circular como la pendular utilizando la funcionalidad del sistema inercial RunScribe®. Objetivos. Este estudio plantea el objetivo principal de ver si la técnica circular es más eficiente tras valorar ciertos parámetros con el sistema inercial Runscribe®. Por otro lado, propone otros objetivos más secundarios relacionados con la técnica de carrera utilizada y la carga generada en los miembros inferiores, además de la relación con el drop del calzado deportivo y los valores del FPI que presentan el sujeto analizado. Metodología. Se llevará a cabo un análisis transversal y descriptivo a 50 corredores habituales, sin lesiones, comprendidos entre 18 y 40 años pertenecientes al Club de Atletismo Safor Teika de Gandía. En él realizaremos una encuesta de valoración de calzado, valoraremos qué posición presenta el pie mediante el FPI y analizaremos los parámetros “Footstrike Type”, “Stride Lenght”, “Shock” y “Efficiency” del Sofware de RunScribe®. Conclusiones. La técnica de carrera circular es más eficiente4,11,12 que la pendular mientras que un drop más alto aumenta la necesidad de un apoyo posterior29. Es necesaria más bibliografía para cumplir el resto de los objetivos. Por tanto, es fundamental la propuesta de estudio realizada

Wearable IMU for Shoulder Injury Prevention in Overhead Sports

Body-worn inertial sensors have enabled motion capture outside of the laboratory setting. In this work, an inertial measurement unit was attached to the upper arm to track and discriminate between shoulder motion gestures in order to help prevent shoulder over-use injuries in athletics through real-time preventative feedback. We present a detection and classification approach that can be used to count the number of times certain motion gestures occur. The application presented involves tracking baseball throws and volleyball serves, which are common overhead movements that can lead to shoulder and elbow overuse injuries. Eleven subjects are recruited to collect training, testing, and randomized validation data, which include throws, serves, and seven other exercises that serve as a large null class of similar movements, which is analogous to a realistic usage scenario and requires a robust estimator

Centre of pressure estimation during walking using only inertial-measurement units and end-to-end statistical modelling

Estimation of the centre of pressure (COP) is an important part of the gait analysis, for example, when evaluating the functional capacity of individuals affected by motor impairment. Inertial measurement units (IMUs) and force sensors are commonly used to measure gait characteristic of healthy and impaired subjects. We present a methodology for estimating the COP solely from raw gyroscope, accelerometer, and magnetometer data from IMUs using statistical modelling. We demonstrate the viability of the method using an example of two models: a linear model and a non-linear Long-Short-Term Memory (LSTM) neural network model. Models were trained on the COP ground truth data measured using an instrumented treadmill and achieved the average intra-subject root mean square (RMS) error between estimated and ground truth COP of 12.3mm and the average inter-subject RMS error of 23.7mm which is comparable or better than similar studies so far. We show that the calibration procedure in the instrumented treadmill can be as short as a couple of minutes without the decrease in our model performance. We also show that the magnetic component of the recorded IMU signal, which is most sensitive to environmental changes, can be safely dropped without a significant decrease in model performance. Finally, we show that the number of IMUs can be reduced to five without deterioration in the model performance.Comment: 21 page

An investigation of the contribution of different turn speeds during standing turns in individuals with and without Parkinson’s disease

Issues around turning can impair daily tasks and trigger episodes of freezing of gait in individuals with Parkinson's disease (PD). Slow speeds associated with aging produce a more en-bloc movement strategy which have been linked with falls while turning. However, the influence of speed of turning on the complex whole-body coordination considering eye movements, turning kinematics, and stepping characteristics during turning has not been examined. The aim of this study was to investigate if individuals with PD have a different response to changes in turning speed compared to healthy older adults during 180° standing turns. 20 individuals with PD and 20 healthy age matched adults participated in this study. Data were collected during clockwise and counter-clockwise turns at three self-selected speeds in a randomised order: (a) normal; (b) faster than normal; and (c) slower than normal. Eye movement and turning kinematics were investigated using electrooculography and Inertial Measurement Units. Mixed Model Analysis of Variance (MM ANOVA) tests with post hoc pairwise comparisons were performed to assess the differences between groups and turning speed. In addition, further post hoc Repeated Measures ANOVA (RM ANOVA) tests were performed if any significant interactions were seen between groups and turning speed. Significant interaction effects were found in eye movement and turning kinematics, and the RM ANOVA showed significant main effects for turning speeds within the PD and the control groups. Turning slowly resulted in similar alterations in eye movement, turning kinematics and stepping characteristics in the PD group and the healthy controls. However, individuals with PD showed a different response to the healthy controls, with a greater delay in eye movement and onset latency of segments in turning kinematics and step variables between the different speeds. These findings help our understanding regarding the turning strategies in individuals with PD. The incorporation of guidance with regard to faster turning speeds may be useful in the management of individuals with PD. Clinical training using different turn directions and speeds may improve coordination, increase confidence and reduce the risk of falling

Toward Real-Time Automated Detection of Turns during Gait Using Wearable Inertial Measurement Units

Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement units. Several different sensor positions (head, back and legs) and three different detection criteria (orientation, angular velocity and both) are compared with regard to their ability to correctly detect turn onset. Furthermore, the different sensor positions are compared with regard to their ability to predict the turn direction and amplitude. The evaluation was performed on ten healthy subjects who performed left/right turns at three amplitudes (22, 45 and 90 degrees). Results showed that turn onset can be most accurately detected with sensors on the back and using a combination of orientation and angular velocity. The same setup also gives the best prediction of turn direction and amplitude. Preliminary measurements with a single amputee were also performed and highlighted important differences such as slower turning that need to be taken into account.ISSN:1424-822

Toward Real-Time Automated Detection of Turns during Gait Using Wearable Inertial Measurement Units

Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement units. Several different sensor positions (head, back and legs) and three different detection criteria (orientation, angular velocity and both) are compared with regard to their ability to correctly detect turn onset. Furthermore, the different sensor positions are compared with regard to their ability to predict the turn direction and amplitude. The evaluation was performed on ten healthy subjects who performed left/right turns at three amplitudes (22, 45 and 90 degrees). Results showed that turn onset can be most accurately detected with sensors on the back and using a combination of orientation and angular velocity. The same setup also gives the best prediction of turn direction and amplitude. Preliminary measurements with a single amputee were also performed and highlighted important differences such as slower turning that need to be taken into account

Toward real-time automated detection of turns during gait using wearable inertial measurement units

Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement units. Several different sensor positions (head, back and legs) and three different detection criteria (orientation, angular velocity and both) are compared with regard to their ability to correctly detect turn onset. Furthermore, the different sensor positions are compared with regard to their ability to predict the turn direction and amplitude. The evaluation was performed on ten healthy subjects who performed left/right turns at three amplitudes (22, 45 and 90 degrees). Results showed that turn onset can be most accurately detected with sensors on the back and using a combination of orientation and angular velocity. The same setup also gives the best prediction of turn direction and amplitude. Preliminary measurements with a single amputee were also performed and highlighted important differences such as slower turning that need to be taken into account