Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer's activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device.

Medical big data: promise and challenges.

The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.

Neuroprotective effect of forsythiaside against transient cerebral global ischemia in gerbil.

Forsythiaside, a phenylethanoside, has been reported to have anti-oxidative activity and memory ameliorating effects against a scopolamine-induced memory deficit model. The aim of this study was to determine whether forsythiaside has neuroprotective activity on transient cerebral global ischemia in gerbil. Transient cerebral ischemia was induced by bilateral common carotid artery occlusion for 5 min and followed by reperfusion for 7 days. Oral administration of forsythiaside was conducted immediately after reperfusion and once a day over the next 7 days. The forsythiaside administration significantly increased the number of viable neurons detected by neuronal nuclei immunostaining and decreased degenerating neuronal cells detected by Fluoro-Jade B staining in the hippocampal CA1 region, at the 7th day post-ischemia (P<0.05). Forsythiaside also significantly decreased the number of ionized calcium-binding adaptor molecule-1-detected activated microglia and glial fibrillary acidic protein-detected astrocytes, both of which were increased after ischemic insults, and decreased interleukin-1ß and tumor necrosis factor-α expression levels, which were also increased after the insults (P<0.05). In addition, forsythiaside significantly improved ischemia-induced cognitive impairments in the Y-maze task (P<0.05). These results suggest that forsythiaside exhibits neuroprotective properties, which are, in part, mediated by its anti-inflammatory activities supported by forsythiaside-induced reductions of activated glial cells and expression levels of interleukin-1ß and tumor necrosis factor-α.

Expression of heat shock protein 60 kDa is upregulated in cervical cancer.

PURPOSE: Cervical cancer caused by the human papilloma virus (HPV) continues to be the cause of yearly death among women. However, it is a curable disease when diagnosed at an early stage. Recently, several researches have reported that heat shock protein (HSP) 60, a chaperone protein of molecular weight of 60 kDa, is involved in carcinogenesis and apoptosis. In order to evaluate the prognostic significance of HSP60 in cervical cancer, we examined differences in the HSP60 expression between cervical cancer and normal tissues in women. MATERIALS AND METHODS: Tissue samples were collected from 20 cervical cancer patients and 20 normal controls. HSP60 expression of cervical cancer and normal tissues were verified by the 2D gel proteomics, semi-quantitative reverse transcriptase polymerase chain reaction (RT-PCR) and Western blot analyses. RESULTS: In 2D proteomic analysis, an increase of HSP60 expression was detected in cervical cancer tissues and confirmed by Western blot analysis (p < 0.05). However, messenger RNA (mRNA) levels of HSP60 did not display any significant differences between cervical cancer and normal tissues. CONCLUSION: These results suggest that HSP60 may be involved in the development of cervical cancer and have profound biological and prognostic significance.