Human Gait Activity Recognition Machine Learning Methods.

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject's quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm's robustness was also verified with the successful detection of freezing gait episodes in a Parkinson's disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.

Expanding the spectrum of KCNJ6-related disorders: Milder phenotype with pathological startle responses.

Keppen-Lubinsky syndrome is caused by pathogenic variants in KCNJ6, which encodes the inwardly rectifying channel subfamily J6. The four confirmed cases reported to date were characterized by severe intellectual disability, global developmental delay, feeding difficulties, and dysmorphic features. All but one of the cases also had a severe form of lipodystrophy, resulting in tightly adherent facial skin and appearance of premature aging. Here, we describe a 36-year-old female with a de novo pathogenic variant in KCNJ6 (NM_002240.5: c.460G>T; p.(Gly154Cys)) presenting with mild intellectual disability, subtle dysmorphic features, obsessive-compulsive disorder, and an exaggerated startle response. This case indicates that KCNJ6-related disorders should be considered in patients with less pronounced dysmorphic features and milder cognitive impairment, as well as in patients with startle disorders.