Cerebral palsy (CP) is a disorder of movement and posture control with multiple impairments. The clinical manifestations of CP vary among children. The aim of this study was to compare the developmental profiles of preschool children with either of two types of CP: spastic diplegic (SD) CP and spastic quadriplegic (SQ) CP. Relationships between the children's various developmental functions were also investigated. We recruited 137 children with spastic CP, aged 1-5 years (mean age = 3.7 +/- 2.1 years), and we classified them into two groups: SD (n = 59) and SQ (n = 78). The comparison group comprised 18 children with typical development. Developmental functions were assessed in all the children, using the Chinese Child Development Inventory with the updated norms. This scale addressed eight functional domains: gross motor ability, fine motor ability, expressive language ability, concept comprehension ability, situation comprehension ability, self-help ability, personal-social skills, and general development. A development quotient (DQ) was determined for each domain as a percentage of the developmental age divided by the chronological age. The developmental profiles of the CP subtypes were found to differ. Children with SQ were found to have lower DQs than those with SD (p < 0.01). There was also a difference in the distribution of DQs between the SD and SQ groups, although the lowest DQ in both groups was for the gross motor domain. An uneven delay in the development of gross motor function was found in both groups of children with CP. Motor functions, including gross motor and fine motor functions, were significantly related to self-help ability. Complex and significant correlations among developmental functions were also identified in children with CP. The findings in the present study may allow clinicians to anticipate the developmental profile of children with CP on the basis of whether they have the SD or SQ subtype. This, in turn, is likely to facilitate individual assessment, goal setting, and the planning of interventions in children with CP.

Recently, surface electromyography (sEMG) has been used to detect running-related works. sEMG provides a non-invasive and real-time method that allows quantification of muscle energy. However, noises in sEMG signals are a serious issue to be considered as these will interrupt the analysis of muscular activity. Hence, this work aims at distinguishing between sEMG valid signals and noises during running exercise by taking advantage of the combination of 3D-CNN and LSTM, which we called 3D-LCNN. Furthermore, according to the possible cases that happen in the sEMG data-collection procedure, we proposed two data-augmentation approaches to expend our sEMG dataset, which are the simulation of the surface electrodes displacement on the skin and the muscle fatigue. Experiment results show that the classification accuracy of the proposed 3D-LCNN can achieve 90.52%. Additionally, this work provides excellent serviceoriented architecture (SOA). The recognition process can be done after the subject placed the sEMG sensors and performed a trial. Therefore, the process can help clinicians or therapists to distinguish between sEMG valid signals and noises more efficiently.

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We study the foot plantar sensor placement by a deep reinforcement learning algorithm without using any prior knowledge of the foot anatomical area. To apply a reinforcement learning algorithm, we propose a sensor placement environment and reward system that aims to optimize fitting the center of pressure (COP) trajectory during the self-selected speed running task. In this environment, the agent considers placing eight sensors within a 7 × 20 grid coordinate system, and then the final pattern becomes the result of sensor placement. Our results show that this method (1) can generate a sensor placement, which has a low mean square error in fitting ground truth COP trajectory, and (2) robustly discovers the optimal sensor placement in a large number of combinations, which is more than 116 quadrillion. This method is also feasible for solving different tasks, regardless of the self-selected speed running task.

This article describes a system for analyzing acoustic data to assist in the diagnosis and classification of children’s speech sound disorders (SSDs) using a computer. The analysis concentrated on identifying and categorizing four distinct types of Chinese SSDs. The study collected and generated a speech corpus containing 2540 stopping, backing, final consonant deletion process (FCDP), and affrication samples from 90 children aged 3–6 years with normal or pathological articulatory features. Each recording was accompanied by a detailed diagnostic annotation by two speech–language pathologists (SLPs). Classification of the speech samples was accomplished using three well-established neural network models for image classification. The feature maps were created using three sets of MFCC parameters extracted from speech sounds and aggregated into a three-dimensional data structure as model input. We employed six techniques for data augmentation to augment the available dataset while avoiding overfitting. The experiments examine the usability of four different categories of Chinese phrases and characters. Experiments with different data subsets demonstrate the system’s ability to accurately detect the analyzed pronunciation disorders. The best multi-class classification using a single Chinese phrase achieves an accuracy of 74.4 percent.