Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Influence of the robotic exoskeleton Lokomat on the control of human gait : an electromyographic and kinematic analysis

Nowadays there is an increasing percentage of elderly people and it is expected that this percentage will continue increasing, carrying huge organizational costs in rehabilitation services. Recent robotic devices for gait training are more and more regarded as alternatives to solve cost-efficiency issues and provide novel approaches for training. Nevertheless, there is a need to address how to target muscular activation and kinematic patterns for optimal recovery after a neurological damage. The main objective of this work was to understand the underlying principles that the human nervous system employs to synchronize muscular activity during walking assisted by Lokomat. A basic low-dimensional locomotor program can explain the synergistic activation of muscles during assisted gait. As a main contribution, we generated a detailed description of the electro myographic and biomechanical response to variations in robotic assistance in intact humans, which can be used for future control strategies to be implemented in motor rehabilitation

Adaptive real-time tool for human gait event detection using a wearable gyroscope

The development of robust algorithms for human gait analysis are essential to evaluate the gait performance, and in many cases, crucial for diagnosing gait pathologies. This work proposes a new adaptive tool for human gait event detection in real-time, based on the angular velocity recorded from one gyroscope placed on the instep of the foot and in a finite state machine with adaptive decision rules. The signal was segmented to detect 6 events: Heel Strike (HS), Foot Flat (FF), Middle Mid-Stance (MMST), Heel-Off (HO), Toe-Off (TO), and Middle Mid-Swing (MMSW). The tool was validated with healthy subjects in ground-level walking using a treadmill, for different speeds (1.5 to 4.5 km/h) and slopes (0 to 10%). The results show that the tool is highly accurate and versatile for the detection of all events, as indicated by the values of accuracy, average delays and advances (HS: 99.96%,-7.95 ms, and 9.85 ms; FF: 99.48%,-4.95 ms, and 9.35 ms; MMST: 98.26%, 36.54 ms, and 16.38 ms; HO: 98.87%,-22.71 ms, and 18.62 ms; TO: 95.95%,-6.80 ms, 14.38 ms; MMSW: 96.06%,-3.45 ms; 0.15 ms, respectively). These findings suggest that the proposed tool is suitable for the real-time gait analysis in real-life activities.- (POCI

Towards human-knee orthosis interaction based on adaptive impedance control through stiffness adjustment

Rehabilitation interventions involving powered, wearable lower limb orthoses that can provide high-challenging locomotor tasks for repetitive training sessions, mainly when assist-as-needed strategies, such as adaptive impedance control, are designed. In this study, the adaptive behavior was ensured by software control of the robotic stiffness involved in the human-knee orthosis interaction in function of the gait cycle and speed. To estimate the stiffness, we analyzed the interaction torque-angle characteristics with experimental data. The speed-stiffness dependency was more evident when high stiffness values are demanded by the user's effort. Experimental evidence from five healthy subjects highlight that the adaptive control strategy provides a more comfortable, natural motion, and kinematic freedom as compared to the trajectory tracking control, allowing the user to contribute to the gait training. Future insights cover the implementation of gravitational compensation and real-time estimation and control of all inner dynamic properties of the impedance control law.This work has been supported by the FCT - Fundacao para a Ciencia e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, with the reference project UID/EEA/04436/2013, and by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalizacao (POCI) - with the reference project POCI-01-0145-FEDER-006941, and partially supported with grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Automatic and real-time locomotion mode recognition of a humanoid robot

Real-time locomotion mode recognition can potentially be applied in the gait analysis as a diagnostic tool or a strategy to control the robotic motion. This research aimed the development of an automatic, accurate and time-effective tool to recognize, in real-time, the locomotion mode that is being performed by a humanoid robot. The proposed strategy should also be general to different walkers and walking conditions. For these purposes, we designed a strategy to identify, in an offline phase, the suitable features and classification models for the real-time recognition. We explored several classification models based on two machine learning approaches using the features previously selected by principal component analysis and genetic algorithm (GA). The validation was carried out for distinct walking directions and speeds of DARwIn-OP. The offline analysis suggests that the most skilled models are the ones created by weighted k-nearest neighbors (KNN), fine KNN, and cubic support vector machine using 2 features selected by GA. Results from the real-time implementation highlight that weighted KNN exhibits a higher recognition performance (accuracy > 99.15%) and a lower elapsed time in the recognition process (89 ms) comparatively to the state-of-the-art. The proposed recognition tool showed to be cost-effective, and highly accurate for the real-time gait analysis at different walking conditions.- (POCI

Powered knee orthosis for human gait rehabilitation: first advances

This paper presents a new system for a powered knee orthosis, that was designed to assist and improve the gait function of patients with gait pathologies. The system contains the orthotic device (embedded with sensors for angle and user-orthosis interaction torque measurements, and an electric actuator) and wearable sensors (inertial measurement unit, force sensitive resistors, and electromyography sensors), which allows the generation of smart rehabilitation tools and several motion assistive techniques. The main goal is to present a conceptual overview and functional description of the system and use scenarios of each component. The attachment mechanism of the orthosis to the limb is also highlighted, being composed of a straps system fixed in the mechanical links of the joint. It was noticed that users with distinct lower-limb morphologies can presents difficulties wearing the orthosis, since the device needs constant adjust to align the mechanical and human joints. The system was validated in ground-level walking on healthy subjects, with emphasis on the impact of the device in the user. The subjects reported that the orthosis is comfortable to use, easy to wear, and no issues were raised regarding the aesthetics of the device. Only the weight was assimilated as a possible hindrance (compensated in the future). Future challenges involve the inclusion of an ankle joint in the system and the use of the proposed tool in rehabilitation.This work is supported by the FCT - Fundacao para a Ciencia e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, with the reference project UID/EEA/04436/2013, and by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalizacao (POCI) - with the reference project POCI-01-0145-FEDER-006941, and partially supported with grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Gait event detection in controlled and real-life situations: repeated measures from healthy subjects

A benchmark and time-effective computational method is needed to assess human gait events in real-life walking situations using few sensors to be easily reproducible. This paper fosters a reliable gait event detection system that can operate at diverse gait speeds and on diverse real-life terrains by detecting several gait events in real time. This detection only relies on the foot angular velocity measured by a wearable gyroscope mounted in the foot to facilitate its integration for daily and repeated use. To operate as a benchmark tool, the proposed detection system endows an adaptive computational method by applying a finite-state machine based on heuristic decision rules dependent on adaptive thresholds. Repeated measurements from 11 healthy subjects (28.27 +/- 4.17 years) were acquired in controlled situations through a treadmill at different speeds (from 1.5 to 4.5 km/h) and slopes (from 0% to 10%). This validation also includes heterogeneous gait patterns from nine healthy subjects (27 +/- 7.35 years) monitored at three self-selected paces (from 1 +/- 0.2 to 2 +/- 0.18 m/s) during forward walking on flat, rough, and inclined surfaces and climbing staircases. The proposed method was significantly more accurate (p > 0.9925) and time effective ( 0.9314) in a benchmarking analysis with a state-of-the-art method during 5657 steps. Heel strike was the gait event most accurately detected under controlled (accuracy of 100%) and real-life situations (accuracy > 96.98%). Misdetection was more pronounced in middle mid swing (accuracy > 90.12%). The lower computational load, together with an improved performance, makes this detection system suitable for quantitative benchmarking in the locomotor rehabilitation field.This work has been supported in part by the Fundacao para a Ciencia e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, by the Reference Project under Grant UID/EEA/04436/2013, and part by the FEDER Funds through the COMPETE 2020-Programa Operacional Competitividade e Internacionalizacao (POCI)-with the Reference Project under Grant POCI-01-0145-FEDER-006941, and in part by Spanish Ministry of Economy and Competitiveness Grant RYC-2014-16613

Outcome measures and motion capture systems for assessing lower limb orthosis-based interventions after stroke: a systematic review

Purpose: To review and categorize, according to the International Classification of Functioning, the outcome measures, and motion capture systems for studying the evidence-based practice of orthotic-based interventions in post-stroke gait rehabilitation. Methods: An electronic literature search was conducted up to February 2018 in Web of Science, Scopus, MEDLINE and Physiotherapy Evidence Database. Randomized trials measuring activity, impairment or participation outcome measures for studying the evidence-based practice of orthoses in gait rehabilitation after an acute or chronic stroke were identified. The studies were assessed through the Cochrane risk-of-bias tool by three authors. Information about stroke’s stage, assessment protocol (goal, timing and motion capture system), orthosis configuration and outcome measures were extracted. Results: Eighteen randomized trials, including 387 post-stroke adults, mostly in the chronic stage, were selected. They assessed 39 outcomes, mainly activity outcome measures such as spatiotemporal (72.2%), kinematic (44.4%) and functional (33.3%) outcomes. Gait speed was the primary outcome in most studies. Participation (22.2%) and impairment (16.7%) outcome measures were less explored. Mostly, non-portable motion capture systems were employed opposing the freely-use of the wearable orthosis. The detection bias risk and the shortage of baseline and follow-up outcome measures affected the studies’ assessment quality. Conclusions: Studies showed heterogeneity in selecting outcomes and timings for assessment. There is evidence for assessing the evidence of orthosis-based gait rehabilitation after stroke through activity outcome measures, primarily the gait speed, recorded by non-wearable motion capture systems. A unified methodology considering wearable sensors for tracking baseline and follow-up measures is needed.Implications for rehabilitation There is evidence on use activity outcome measures to assess the meaningful evidence-based practice of orthosis-based gait rehabilitatio- (undefined

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

Alternative Approaches in Development of Heterogeneous Titania-Based Photocatalyst

Three alternative approaches for the development of heterogeneous photocatalysts are comparatively evaluated, namely (i) the use of molecular imprinting concept for the development of heterogeneous catalysts employing rhodamine B as template and sol–gel as synthesis route; (ii) the impregnation of TiCl4 on mixed nano- and micro-metric silicas, followed by calcination; (iii) the use of industrial and academic chemical residues as source of potential photocatalyst species impregnated on supports. All tests were carried on with rhodamine B as target molecule. For comparative reasons, photocatalytic tests were carried out with commercial titania (P25). The solids were characterized by nitrogen porosimetry, small-angle X-ray scattering (SAXS), zeta potential (ZP), diffuse reflectance spectroscopy in the ultraviolet region (DRS-UV), diffuse reflectance infrared Fourier transmission spectroscopy (DRIFTS), and Rutherford backscattering spectrometry (RBS). The supported catalysts resulting from silica nanoparticles and residue of the petrochemical industry achieved higher percentage of the dye degradation under ultraviolet (68.0 and 66.8%, respectively) radiation. The industrial waste reached the highest photocatalytic activity under visible (61%) radiation, while the commercial P25 achieved 82.0and 12.3% for ultraviolet and visible radiation, respectively. The textural and structural characteristics of the supported catalyst prepared with fumed silica and petrochemical waste (SiPe), namely the low-energy bandgap (1.8 eV), large surface area (280 m2 g−1), high pore volume (1.9 cm3 g−1), and high zeta potential value (−36.4 mV), may have been responsible for their high activity