Human Posture Transition-Time Detection Based upon Inertial Measurement Unit and Long Short-Term Memory Neural Networks.

As human-robot interaction becomes more prevalent in industrial and clinical settings, detecting changes in human posture has become increasingly crucial. While recognizing human actions has been extensively studied, the transition between different postures or movements has been largely overlooked. This study explores using two deep-learning methods, the linear Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), to detect changes in human posture among three different movements: standing, walking, and sitting. To explore the possibility of rapid posture-change detection upon human intention, the authors introduced transition stages as distinct features for the identification. During the experiment, the subject wore an inertial measurement unit (IMU) on their right leg to measure joint parameters. The measurement data were used to train the two machine learning networks, and their performances were tested. This study also examined the effect of the sampling rates on the LSTM network. The results indicate that both methods achieved high detection accuracies. Still, the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. Additionally, the network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. The accuracies for posture transition time and identification at a sampling rate of 100 Hz were 0.17 s and 94.44%, respectively. In summary, this study achieved some good outcomes and laid a crucial foundation for the engineering application of digital twins, exoskeletons, and human intention control.

Machine Learning for Human Motion Intention Detection.

The gait pattern of exoskeleton control conflicting with the human operator's (the pilot) intention may cause awkward maneuvering or even injury. Therefore, it has been the focus of many studies to help decide the proper gait operation. However, the timing for the recognization plays a crucial role in the operation. The delayed detection of the pilot's intent can be equally undesirable to the exoskeleton operation. Instead of recognizing the motion, this study examines the possibility of identifying the transition between gaits to achieve in-time detection. This study used the data from IMU sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully separate the transition period from the motion periods. 2. The detection of gait change from walking to sitting can be as fast as 0.17 s, which is adequate for future control applications. However, detecting the transition from standing to walking can take as long as 1.2 s. 3. This study also find that the network trained for one person can also detect movement changes for different persons without deteriorating the performance.

Correlation between corneal dynamic responses and keratoconus topographic parameters.

OBJECTIVE: To investigate the correlation between corneal biomechanical properties and topographic parameters using machine learning networks for automatic severity diagnosis and reference benchmark construction. METHODS: This was a retrospective study involving 31 eyes from 31 patients with keratonus. Two clustering approaches were used (i.e., shape-based and feature-based). The shape-based method used a keratoconus benchmark validated for indicating the severity of keratoconus. The feature-based method extracted imperative features for clustering analysis. RESULTS: There were strong correlations between the symmetric modes and the keratoconus severity and between the asymmetric modes and the location of the weak centroid. The Pearson product-moment correlation coefficient (PPMC) between the symmetric mode and normality was 0.92 and between the asymmetric mode and the weak centroid value was 0.75. CONCLUSION: This study confirmed that there is a relationship between the keratoconus signs obtained from topography and the corneal dynamic behaviour captured by the Corvis ST device. Further studies are required to gather more patient data to establish a more extensive database for validation.

NSSR1 promotes neuronal differentiation of mouse embryonic carcinoma P19 cells.

We generated the small interference RNAs to specifically silence the expression of neural salient serine/arginine rich protein 1 (NSSR1) and showed that the inhibition of NSSR1 expression in mouse embryonic carcinoma cells (P19) reduces neuronal differentiation. By contrast, its over-expression promotes the differentiation. Neither inhibition nor over-expression shows distinct effect on cell proliferation. The over-expression increases the inclusion of NCAM L1 exon2 while the inhibition reduces the inclusion. The splicing of kinase insert free isoform of TrkC (TrkC-K1) is increased by the over-expression. The results demonstrate that NSSR1 promotes neuronal differentiation and the splicing of NCAML1 exon2 and TrkC-K1.

Neural expression and regulation of NSSR1 proteins.

Neural salient serine-/arginine-rich protein 1 (NSSR1) is a newly identified SR protein that regulates pre-mRNA splicing. In the present study, we demonstrated the neural specialization of NSSR1 protein expression in humans and mice. Strong immunoreactive signals to NSSR1 were observed in mouse cerebral neurons, cerebellar Purkinje cells, pyramidal neurons in CA1, CA2 and CA3 regions of the hippocampus and granule cells in the dentate gyrus. In primarily cultured mouse neural progenitor cells (NPCs), at the undifferentiated status, NSSR1 transcripts were detected, but not the proteins. In comparison, in differentiated NPCs both NSSR1 transcripts and proteins were expressed and significantly up-regulated. The results suggest that NSSR1 is important in regulation of brain function and neural differentiation, possibly via regulating the neural-specific alternative splicing of genes.

[Preparation and identification of rabbit-anti-human nestin antiserum].

Nestin belongs to the class VI intermediate filament family and it is a marker for neural progenitor cells. In this work, the 3'-terminal coding sequence(396 bp) of human nestin gene was cloned into pGEX-3X plasmid and introduced into BL21 E. coli cells. The GST-nestin protein was purified with an affinity column. Anti-human nestin antiserum was raised by immunizing a rabbit with the fusion protein. The high specificity of the antibody against human nestin was confirmed by western-blot and immunocytochemistry analysis.