A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (-0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As align ment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥ 92%, precision ≥ 97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson's Disease patients

Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson's disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson's disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%), slalom walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%), and turning (IC: recall [Formula: see text] 85%, precision [Formula: see text] 95%, F1 score [Formula: see text]91%; FC: recall [Formula: see text] 84%, precision [Formula: see text] 95%, F1 score [Formula: see text]89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events

Characteristics of muscle activation patterns at the ankle in stroke patients during walking.

Stroke causes impairment of the sensory and motor systems; this can lead to difficulties in walking and participation in society. For effective rehabilitation it is important to measure the essential characteristics of impairment and associate these with the nature of disability. Efficient gait requires a complex interplay of muscles. Surface electromyography(sEMG) can be used to measure muscle activity and to observe disruption to this interplay after stroke. Yet, classification of this disruption in stroke patients has not been achieved. It is hypothesised that features identified from the sEMG signal can be used to classify underlying impairments. A clinically viable gait analysis system has been developed, integrating an in-house wireless sEMG system synchronised with bilateral video and inertial orientation sensors. Signal processing techniques have been extended and implemented, appropriate for use with sEMG. These techniques have focussed on frequency domain features using wavelet analysis and muscle activation patterns using principal component analysis. The system has been used to measure gait from stroke patients and un-impaired subjects. Characteristic patterns of activity from the ankle musculature were defined using principal component analysis of the linear envelope. Patients with common patterns of tibialis anterior activity did not necessarily share common patterns of gastrocnemius or soleus activity. Patients with similar linear envelope patterns did not always present with the same kinematic profiles. The relationship between observable impairments, kinematics and sEMG is seen to be complex and there is therefore a need for a multidimensional view of gait data in relation to stroke impairment. The analysis of instantaneous mean frequency and time-frequency has revealed additional periods of activity not obvious in the linear or raw signal representation. Furthermore, characteristic calf activity was identified that may relate to abnormal reflex activity. This has provided additional information with which to group characteristic muscle activity. An evaluation of the co-activation of gastrocnemius and tibialis anterior muscles using a sub-band filtering technique revealed three groups; those with distinct co-activation, those with little co-activation and those with continuous activity in the antagonistic pair across the stride. Signal features have been identified in sEMG recordings from stroke patients whilst walking extending current signal processing techniques. Common features of the sEMG and movement have been grouped creating a decision matrix. These results have contributed to the field of clinical measurement and diagnosis because interpretation of this decision matrix is related to underlying impairment. This has provided a framework from which subsequent studies can classify characteristic patterns of impairment within the stroke population; and thus assist in the provision of rehabilitative interventions

Railway foreign body vibration signal detection based on wavelet analysis

Based on the wavelet packet analysis method with time-frequency analysis characteristics, the measurement signal of the vibration system is processed for noise reduction, the soft-hard threshold compromise wavelet denoising method used has the advantages of soft threshold and hard threshold denoising, and through the introduction of compromise factors, signal processing can be performed more flexibly in signal analysis. For the denoised signal, the fundamental wavelet time-energy spectrum analysis, the main components of the signal can be clearly displayed, and according to the distribution of its energy in each frequency band, the signal characteristics can be displayed intuitively. Experimental results show: It can be determined that there is a foreign body intrusion incident at a position 520 m away from the monitoring point, rather than a normal train travel incident. In fact, we are walking back and forth at a distance of about 520 m from the monitoring point, simulating the intrusion of illegal foreign objects such as pedestrians and livestock beside the railroad tracks prove that analysis and judgment can be known, the wavelet analysis proposed by the author can realize the monitoring and judgment of some illegal foreign body intrusion incidents such as pedestrians and livestock

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go

Inertial Measurement Unit-Based Gait Event Detection in Healthy and Neurological Cohorts: A Walk in the Dark

A deep learning (DL)-based network is developed to determine gait events from IMU data from a shank- or foot-worn device. The DL network takes as input the raw IMU data and predicts for each time step the probability that it corresponds to an initial or final contact. The algorithm is validated for walking at different self-selected speeds across multiple neurological diseases and both in clinical research settings and the habitual environment. The algorithms shows a high detection rate for initial and final contacts, and a small time error when compared to reference events obtained with an optical motion capture system or pressure insoles. Based on the excellent performance, it is concluded that the DL algorithm is well suited for continuous long-term monitoring of gait in the habitual environment

Muscle Force Estimation and Fatigue Detection Based on sEMG Signals

Ph.DDOCTOR OF PHILOSOPH

Epilepsy

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Associations of pulmonary parameters with accelerometer data

Some papers of this thesis are not available in Munin: Paper 2. Dias, A.; Gorzelniak, L.; Jorres, R.; Fischer, R.; Hartvigsen, G.; Horsch,A.: 'Assessing Physical Activity in the daily life of cystic fibrosis patients', Journal of Pervasive Computing (2012), vol. 8(6):837–844. Available at http://dx.doi.org/10.1016/j.pmcj.2012.08.001 Paper 3. Gorzelniak, L.; Dias, A.; Schultz,K.; Wittmann, M.; Karrasch, S.; Jorres, R.; Horsch,A.: 'Comparison of recording positions of physical activity in severe COPD', Journal Of Chronic Obstructive Pulmonary Disease (2012), vol. 9(5):528-537. Available at http://dx.doi.org/10.3109/15412555.2012.708066 Paper 4. Dias, A.; Gorzelniak, L.; Schultz,K.;Wittmann, M.; Rudnik, J.;Jorres, R.; Horsch,A.: 'Classification of exacerbation episodes in Chronic Obstructive Pulmonary Disease patients' (manuscript) Paper 5. Ortlieb, S.; Gorzelniak, L.; Dias,A.; Schulz, H.; Horsch,A.: 'Recommendations for Collecting and Processing Accelerometry Data in Older Healthy People' (manuscript) Additional paper 1. Dias, A.; Gorzelniak, L.; Doring, A.; Hartvigsen, G.; Horsch, A.: 'Extracting Gait Parameters from Raw Data Accelerometers', Studies in Health Technology and Informatics (2011), vol. 169:445-449. Additional paper 2. Gorzelniak, L.; Dias, A.; Soyer, H.; Knoll, A.; Horsch, A.; 'Using a Robotic Arm to Assess the Variability of Motion Sensors', Studies in Health Technology and Informatics (2011), vol. 169:897-901. Additional paper 3. Chen, C.; Dias, A.; Knoll, A.; Horsch, A.: 'A Prototype of a Wireless Body Sensor Network for Healthcare Monitoring', Medical informatics in Europe (2011). Additional paper 4. Skrovseth, S.; Dias, A.; Gorzelniak, L.; Godtliebsen, F.; Horsch, A.: 'Scale-space methods for live processing of sensor data', Medical informatics in Europe (2012). Additional paper 7. Peters A, Döring A, Ladwig KH, Meisinger C, Linkohr B, Autenrieth C, Baumeister SE, Behr J, Bergner A, Bickel H, Bidlingmaier M, Dias A, Emeny RT, Fischer B, Grill E, Gorzelniak L, Hänsch H, Heidbreder S, Heier M, Horsch A, Huber D, Huber RM, Jörres RA, Kääb S, Karrasch S, Kirchberger I, Klug G, Kranz B, Kuch B, Lacruz ME, Lang O, Mielck A, Nowak D, Perz S, Schneider A, Schulz H, Müller M, Seidl H, Strobl R, Thorand B, Wende R, Weidenhammer W, Zimmermann AK, Wichmann HE, Holle R.: 'Multimorbidity and successful aging: the populationbased KORA-Age study', Zeitschrift für Gerontologie und Geriatrie (2011), vol. 44(2):41-54. Available at http://dx.doi.org/10.1007/s00391-011-0245-7In Europe it is estimated that the number of elderly people aged above 65 will have doubled by 2060. In several chronic pulmonary diseases patients can suffer recurrent exacerbation episodes that can lead to severe breathing or death. In this thesis we explore the association of physical activity to lung health parameters, focusing on cystic fibrosis and chronic obstructive pulmonary disease patients and a group of the general population. The main goals of the thesis were to assess the feasibility of classifying exacerbation episodes in cystic fibrosis and chronic obstructive pulmonary disease patients and to implement new parameters in the context of a cohort study. We conducted four distinct studies involving in total over 250 subjects. We asked them to wear a set of accelerometers, including GT3X and RT3, recording physical activity for up to 14 days. The data was processed and several features extracted that were used as inputs in three different classification algorithms: logarithmic regression, neural networks and support vector machines. We achieved an area under the curve of 67% with logarithmic regression, 83% with neural networks and 90% with support vector machines when classifying exacerbation episodes in chronic obstructive pulmonary disease. A neural network was achieved an accuracy of 85% distinguishing cystic fibrosis patients from healthy controls. We proposed, extracted and tested a large set of physical activity parameters for use in KORA-Age. The work on classification of exacerbations in COPD patients is, to our knowledge, the first attempt based on features from accelerometer data. Overall SVM showed to be the most robust classifier with an area under the curve of 90%. Nevertheless the number of patients and episodes is too low to draw definitive conclusions. The next step to classify exacerbations in COPD is to design a study with a statistically significant number of exacerbation episodes