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- Péter, Annamária, et al.
(författare)
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Comparing Surface and Fine-Wire Electromyography Activity of Lower Leg Muscles at Different Walking Speeds.
- 2019
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Ingår i: Frontiers in Physiology. - : Frontiers Media S.A.. - 1664-042X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Ankle plantar flexor muscles are active in the stance phase of walking to propel the body forward. Increasing walking speed requires increased plantar flexor excitation, frequently assessed using surface electromyography (EMG). Despite its popularity, validity of surface EMG applied on shank muscles is mostly unclear. Thus, we examined the agreement between surface and intramuscular EMG at a range of walking speeds. Ten participants walked overground at slow, preferred, fast, and maximum walking speeds (1.01 ± 0.13, 1.43 ± 0.19, 1.84 ± 0.23, and 2.20 ± 0.38 m s-1, respectively) while surface and fine-wire EMG activities of flexor hallucis longus (FHL), soleus (SOL), medial gastrocnemius (MG) and lateral gastrocnemius (LG), and tibialis anterior (TA) muscles were recorded. Surface and intramuscular peak-normalised EMG amplitudes were compared for each muscle and speed across the stance phase using Statistical Parametric Mapping. In FHL, we found differences around peak activity at all speeds except fast. There was no difference in MG at any speed or in LG at slow and preferred speeds. For SOL and LG, differences were seen in the push-off phase at fast and maximum walking speeds. In SOL and TA, surface EMG registered activity during phases in which intramuscular EMG indicated inactivity. Our results suggest that surface EMG is generally a suitable method to measure MG and LG EMG activity across several walking speeds. Minimising cross-talk in FHL remains challenging. Furthermore, SOL and TA muscle onset/offset defined by surface EMG should be interpreted cautiously. These findings should be considered when recording and interpreting surface EMG of shank muscles in walking.
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- Péter, Annamária, et al.
(författare)
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Effect of footwear on intramuscular EMG activity of plantar flexor muscles in walking.
- 2020
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Ingår i: Journal of Electromyography & Kinesiology. - : Elsevier. - 1050-6411 .- 1873-5711. ; 55
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Tidskriftsartikel (refereegranskat)abstract
- One of the purposes of footwear is to assist locomotion, but some footwear types seem to restrict natural foot motion, which may affect the contribution of ankle plantar flexor muscles to propulsion. This study examined the effects of different footwear conditions on the activity of ankle plantar flexors during walking. Ten healthy habitually shod individuals walked overground in shoes, barefoot and in flip-flops while fine-wire electromyography (EMG) activity was recorded from flexor hallucis longus (FHL), soleus (SOL), and medial and lateral gastrocnemius (MG and LG) muscles. EMG signals were peak-normalised and analysed in the stance phase using Statistical Parametric Mapping (SPM). We found highly individual EMG patterns. Although walking with shoes required higher muscle activity for propulsion than walking barefoot or with flip-flops in most participants, this did not result in statistically significant differences in EMG amplitude between footwear conditions in any muscle (p > 0.05). Time to peak activity showed the lowest coefficient of variation in shod walking (3.5, 7.0, 8.0 and 3.4 for FHL, SOL, MG and LG, respectively). Future studies should clarify the sources and consequences of individual EMG responses to different footwear.
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- Seynnes, Olivier R, et al.
(författare)
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Ultrasound-Based Testing Of Tendon Mechanical Properties : A Critical Evaluation.
- 2015
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Ingår i: Journal of applied physiology. - : American Physiological Society. - 8750-7587 .- 1522-1601. ; 118:2, s. 133-141
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Tidskriftsartikel (refereegranskat)abstract
- In the past twenty years, the use of ultrasound-based methods has become a standard approach to measure tendon mechanical properties in vivo. Yet, the multitude of methodological approaches adopted by various research groups probably contributes to the large variability of reported values. The technique of obtaining and relating tendon deformation to tensile force in vivo has been applied differently, depending on practical constraints or scientific points of view. Divergence can be seen in i) methodological considerations such as the choice of anatomical features to scan and to track, force measurements or signal synchronisation and ii), in physiological considerations related to the viscoelastic behaviour or length measurements of tendons. Hence, the purpose of the present review is to assess and discuss the physiological and technical aspects connected to in vivo testing of tendon mechanical properties. In doing so, our aim is to provide the reader with a systematic, qualitative analysis of ultrasound-based techniques. Finally, a list of recommendations is proposed for a number of selected issues.
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- Su, Binbin
(författare)
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Human motion prediction using wearable sensors and machine Learning
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Accurately measuring and predicting human movement is important in many contexts, such as in rehabilitation and the design of assistive devices. Thanks to the development and availability of a wide variety of sensors, scientists study human movement in many settings and capture characteristic properties unique to individuals as well as to larger study populations. Inertial measurement units (IMU), which contain accelerometers and gyroscopes, measure segment accelerations and angular velocities, and electromyography (EMG) sensors measure muscle excitation. These types of wearable sensors can be donned at the same time and can record data at a high frequency, potentially resulting in a large amount of data. Machine learning (ML) is an effective tool to extract the prominent features and make statistical inferences from data and has the potential to enhance human motion analyses through data-driven prediction. The overall aim of this thesis was to predict human motion through data-driven approaches and musculoskeletal simulations using wearable sensors and ML. A deterministic machine learning approach using a convolutional neural network (CNN) was first proposed to segment gait cycles into five phases based on experimental IMU data in subjects at different walking speeds. The proposed CNN was able to capture kinematic characteristics in raw IMU data, such as linear acceleration, rotational velocity, and magnetic field, and distinguish different gait phases. In recognizing all gait phases, it achieved an overall accuracy of 97.5% on a well-trained model, with up to 99.6% accuracy in detecting the swing phase. Our results also showed walking speed did not have a major influence on the overall gait phase recognition accuracy for people with typical gait patterns. However, while the swing was most accurately recognized, the terminal stance was least accurately recognized, and even more so at lower walking speeds.We then developed a long short-term (LSTM) network to predict both gait phase (loading response, midstance, terminal stance, pre-swing, and swing) and gait trajectory (angular velocities of thigh, shank, and foot segments) in up to the subsequent 200ms, based on immediately prior data. The overall accuracy of gait phase prediction was up to 94%, with the swing phase the most accurately predicted (97%). Our results also showed a high correlation between predicted and true values of the angular velocity of the thigh, shank, and foot segments. People walk on different terrains daily, for instance, level-ground walking, ramp/stairs ascent/descent, and stepping over obstacles. Movements patterns change as people move from one terrain to another, i.e. transition from one locomotion mode to another. Locomotion modes are typically labeled between two gait events, foot contact (FC) and toe off (TO). Since there is no exact instance for discriminating the transition between two locomotion modes, we identified TO as the critical gait event. We integrated locomotion mode prediction and gait event identification into one machine learning framework comprising two multilayer perceptrons (MLP), using fused data from two types of wearable sensors, namely EMG sensors and IMUs. The first MLP successfully identified FC and TO; FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 and -5.3 ms for FC and TO respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time before the critical events, using EMG and IMU signals as input features.Data-driven approaches using wearable sensors are incapable of modeling the mechanism between neuromuscular control and wearable sensor outputs. Musculoskeletal simulation can, on the other hand, explain the interactions between muscular control, kinematics, and kinetics in human motion. Thus, we integrated a reinforcement learning algorithm, a reflex-based controller, and a musculoskeletal model including trunk, pelvis, and leg segments to simulate reasonably realistic human walking at different speeds. We further generated pathological gaits that may result from ankle plantarflexor weakness using the same approach. The simulated hip and knee angles correlate reasonably well with reported experimental data, though less so for ankle kinematics. The computed muscle excitations in major low limb muscles largely correspond to the expected on-off timing of these muscles during walking. In summary, the studies in this thesis describe and predict human movement with wearable sensors and machine learning algorithms. We detected and predicted gait phases and events, predicted segment movements and identified intended transitions between walking modes during the stance phase of the previous gait cycle on the same side, before the step into the new mode, all based on data from wearable sensors. This has important potential implications in continuous monitoring and analysis of a person's movements outside a lab environment. The musculoskeletal simulation provided insight into the relationship between neuromuscular control and sensory feedback, which could also be applied to better understand and predict likely changes in gait when changes occur in neuromuscular control. Our approaches combining wearable sensors and machine learning could be ultimately applied to facilitate the design of exoskeletons that can provide seamless assistance for people with motor disorders.
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