Pedaling improves gait ability of hemiparetic patients with stiff-knee gait: fall prevention during gait

Fujita, Kazuki; Kobayashi, Yasutaka; Miaki, Hiroichi; Hori, Hideaki; Tsushima, Yuichi; Sakai, Ryo; Nomura, Tomomi; Ogawa, Tomoki; Kinoshita, Haruhito; Nishida, Tomoko; Hitosugi, Masahito

doi:10.1016/j.jstrokecerebrovasdis.2020.105035

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…Pelvic obliquity (hip hiking) also facilitates foot clearance with greater energy efficiency [ 28 , 29 ]. This gait is most likely a reflection of an altered motor template occurring after a stroke, which contributes to a decline in gait ability [ 83 , 84 ]. Home-based stroke hemiplegia patients tend to fall easily due to poor toe clearance, which is reported to be one of the causes of falling, although there are many other related factors [ 85 ].…”

Section: Discussionmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Chen

et al. 2022

Sensors

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Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.

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Section: Discussionmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Chen

et al. 2022

Sensors

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“…This was mainly explained by differences in mAS 4ceps and MRC ankle results. However, these studies [ 22 , 50 , 51 ] included very small sample sizes (13 to 21 SKG patients) and the study [ 51 ] only included patients with mAS 4ceps ≥ 1+, which could explain the higher median value observed for this clinical parameter (3 versus 1, Table 5 ). In our study, mAS 4ceps that is frequently called Duncan-Ely or Ely test in clinical routine, was assessed but not used as an including criteria since it was shown that this test is not predictive of abnormal electrical activity of the rectus femoris muscle during gait in post-stroke SKG patients [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

“…In patients with upper motor neuron injury, a quantitative metric of unilateral SKG severity is still lacking [ 19 ] and no specific classification has yet been developed, perhaps due to the lack of sufficiently large samples of patients in whom quantitative gait analysis has been performed and to the complexity of classifying patients with very heterogeneous knee gait patterns [ 7 ]. In clinical practice, sagittal plane knee excursion may be almost (<10°) or completely absent in some post-stroke patients throughout the swing phase [ 3 , 20 ], while a group of less impaired subjects may maintain some knee mobility and achieve a mean peak flexion angle of about 20 to 40° [ 4 , 5 , 8 , 20 , 21 , 22 ]. Specific kinematic curves of the knee joint showing a “double bump” during the swing phase, i.e., two flexion angle peaks separated by a distinct extension movement, can also be found in some stroke patients [ 15 , 18 ] and may interfere with the interpretation of visual observation made by clinicians.…”

Section: Introductionmentioning

confidence: 99%

Classification of Stiff-Knee Gait Kinematic Severity after Stroke Using Retrospective k-Means Clustering Algorithm

Chantraine¹,

Schreiber²,

Pereira³

et al. 2022

JCM

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Nowadays, a classification system for unilateral stiff-knee gait (SKG) kinematic severity in hemiparetic adult patients after stroke does not exist. However, such classification would be useful to the clinicians. We proposed the use of the k-means method in order to define unilateral SKG severity clusters in hemiparetic adults after stroke. A retrospective k-means cluster analysis was applied to five selected knee kinematic parameters collected during gait in 96 hemiparetic adults and 19 healthy adults from our clinical gait analysis database. A total of five discrete knee kinematic clusters were determined. Three clusters of SKG were identified, based on which a three-level severity classification was defined: unbend-knee gait, braked-knee gait, and frozen-limb gait. Preliminary construct validity of the classification was obtained. All selected knee kinematic parameters defining the five clusters and the majority of usual kinematic parameters of the lower limbs showed statistically significant differences between the different clusters. We recommend diagnosing SKG for values strictly below 40° of knee flexion during the swing phase. Clinicians and researchers are now able to specify the level of kinematic severity of SKG in order to optimize treatment choices and future clinical trial eligibility criteria.

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“…The sampling frequency of the inertial sensors was set at 100 Hz. MyoMOTION inertial sensors were placed according to the lower limb rigid-body model with seven joint segments used in MR3 software (Noraxon Inc.), as follows: on shoes (top of the upper foot), front of the shanks, front of the thighs, and the bony area of the sacrum [ 19 ]. Calibration was performed in an upright position to determine the value of the 0° angle in the joints studied.…”

Section: Methodsmentioning

confidence: 99%

Kinematic and Electrophysiological Characteristics of Pedal Operation by Elderly Drivers during Emergency Braking

Fujita¹,

Kobayashi²,

Sato

et al. 2021

Healthcare

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Age-related decline in lower limb motor control may cause errors in pedal operation when driving a car. This study aimed to clarify the kinematics and electrophysiological characteristics of the pedal-switching operation associated with emergency braking in the case of elderly drivers. The participants in this study consisted of 11 young drivers and 10 elderly drivers. An experimental pedal was used, and the muscle activity and kinematic data during braking action were analyzed using the light from a light-emitting diode installed in the front as a trigger. The results showed that elderly drivers took the same time from viewing the visual stimulus to releasing the accelerator pedal as younger drivers, but took longer to switch to the brake pedal. The elderly drivers had higher soleus muscle activity throughout the process, from accelerator release to brake contact; furthermore, the rectus femoris activity was delayed, and the simultaneous activity between the rectus femoris and biceps femoris was low. Furthermore, elderly drivers tended to have low hip adduction velocity and tended to switch pedals by hip internal rotation. Thus, the alteration in joint movements and muscle activity of elderly drivers can reduce their pedal operability and may be related to the occurrence of pedal errors.

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Pedaling improves gait ability of hemiparetic patients with stiff-knee gait: fall prevention during gait

Cited by 11 publications

References 37 publications

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Classification of Stiff-Knee Gait Kinematic Severity after Stroke Using Retrospective k-Means Clustering Algorithm

Kinematic and Electrophysiological Characteristics of Pedal Operation by Elderly Drivers during Emergency Braking

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