General movements (GMs), a type of spontaneous movement, have been used for the early diagnosis of infant disorders. In clinical practice, GMs are visually assessed by qualified licensees; however, this presents a difficulty in terms of quantitative evaluation. Various measurement systems for the quantitative evaluation of GMs track target markers attached to infants; however, these markers may disturb infants' spontaneous movements. this paper proposes a markerless movement measurement and evaluation system for GMs in infants. The proposed system calculates 25 indices related to GMs, including the magnitude and rhythm of movements, by video analysis, that is, by calculating background subtractions and frame differences. Movement classification is performed based on the clinical definition of GMs by using an artificial neural network with a stochastic structure. This supports the assessment of GMs and early diagnoses of disabilities in infants. in a series of experiments, the proposed system is applied to movement evaluation and classification in full-term infants and lowbirth-weight infants. The experimental results confirm that the average agreement between four GMs classified by the proposed system and those identified by a licensee reaches up to 83.1 ± 1.84%. In addition, the classification accuracy of normal and abnormal movements reaches 90.2 ± 0.94%.

No abstract

One of the recommended post-stroke gait rehabilitation treatments is the use of an ankle-foot orthosis. In clinical practice, it is important to adjust the torque of the ankle-foot orthosis assistance to suit each patient's body function and gait ability. The present study aimed to investigate the effect of changing the plantar flexion resistance of the ankle-foot orthosis on the post-stroke gait kinematics and kinetics during the early stance phase using a musculoskeletal model and an ankle-foot orthosis model. The subject was a male with post-stroke left hemiplegia who could walk independently without an ankle-foot orthosis and/or cane. The subject walked at a self-selected speed under the no ankle-foot orthosis condition and three ankle-foot orthosis conditions, each with a different plantar flexion resistive torque. A motion analysis system was used to measure the following spatiotemporal parameters: gait speed, step length, cadence, and step length ratio. In addition, the ankle angle of the paretic side, ankle torque of the paretic side, and plantar flexion resistance torque of the ankle-foot orthosis were calculated using a musculoskeletal model and an ankle-foot orthosis model. The results showed that the gait speed and step length ratio of all ankle-foot orthosis conditions were improved compared with the no ankle-foot orthosis condition. In particular, the condition with the smallest torque was the most symmetric of the four walking conditions. The condition with the smallest torque also resulted in the greatest increase in the dorsiflexion angle of the paretic side at heel contact. The internal dorsiflexion torque was most increased in the ankle-foot orthosis condition with the smallest torque for this subject. The simulation of the post-stroke gait in the present study contributes to the development of more effective gait rehabilitation treatment methods using an ankle-foot orthosis.

Early intervention is now considered the core treatment strategy for autism spectrum disorders (ASD). Thus, it is of significant clinical importance to establish a screening tool for the early detection of ASD in infants. To achieve this goal, in a longitudinal design, we analyzed spontaneous bodily movements of 4-month-old infants from general population and assessed their ASD-like behaviors at 18 months of age. A total of 26 movement features were calculated from video-recorded bodily movements of infants at 4 months of age. Their risk of ASD was assessed at 18 months of age with the Modified Checklist for Autism in Toddlerhood, a widely used screening questionnaire. Infants at high risk for ASD at 18 months of age exhibited less rhythmic and weaker bodily movement patterns at 4 months of age than low-risk infants. When the observed bodily movement patterns were submitted to a machine learning-based analysis, linear and non-linear classifiers successfully predicted ASD-like behavior at 18 months of age based on the bodily movement patterns at 4 months of age, at the level acceptable for practical use. This study analyzed the relationship between spontaneous bodily movements at 4 months of age and the ASD risk at 18 months of age. Experimental results suggested the utility of the proposed method for the early screening of infants at risk for ASD. We revealed that the signs of ASD risk could be detected as early as 4 months after birth, by focusing on the infant’s spontaneous bodily movements.

No abstract