Spectral Collaborative Representation based Classification for hand gestures recognition on electromyography signals

AbstractThe classification of the bio-signal has been used for various purposes in the literature as they are versatile in diagnosis of anomalies, improvement of overall health and sport performance and creating intuitive human computer interfaces. However, automatic identification of the signal patterns on a streaming real-time signal requires a series of complex procedures. A plethora of heuristic methods, such as neural networks and fuzzy systems, have been proposed as a solution. These methods stipulate certain conditions, such as preconditioning the signals, manual feature selection and large number of training samples.In this study, we introduce a novel variant and application of the Collaborative Representation based Classification (CRC) in spectral domain for recognition of hand gestures using raw surface electromyography (EMG) signals. The CRC based methods do not require large number of training samples for an efficient pattern classification. Additionally, we present a training procedure in which a high end subspace clustering method is employed for clustering the representative samples into their corresponding class labels. Thereby, the need for feature extraction and spotting patterns manually on the training samples is obviated.We presented the intuitive use of spectral features via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize gestures with higher levels of accuracy for a fairly rich gesture set compared to the available methods. The worst recognition result which is the best in the literature is obtained as 97.3% among the four sets of the experiments for each hand gestures. The recognition results are reported with a substantial number of experiments and labeling computation

Reactivity of CA19-9 and CA125 in Histological Subtypes of Epithelial Ovarian Tumors and Ovarian Endometriosis

Previous reports have shown that some ovarian endometrioid adenocarcinomas and ovarian clear cell adenocarcinomas derive from ovarian endometriosis (OE), and that endocervical-like mucinous borderline ovarian tumors are associated with OE. We examined the relationship between the staging and histological subtypes of OE or epithelial ovarian tumors (EOT) and the serum levels of carbohydrate antigen 19-9 (CA19-9) and carbohydrate antigen 125 (CA125) to evaluate the potential of these markers for preoperative diagnosis. First, we analyzed the preoperative serum levels of CA19-9 and CA125 in 195 patients who were histopathologically diagnosed with OE or EOT. We then performed a case-control study in which 308 women were enrolled, the 195 women described above and 113 healthy women as control subjects. Serum CA19-9 and CA125 levels were found to be useful in differentiating between OE and serous adenocarcinoma, but not between OE and other EOT. Moreover, serum CA19-9 levels were useful for preoperative assessment between OE and stage I mucinous borderline ovarian tumors, with or without the interstitial infiltration. In addition, considering that the serum CA19-9 levels in stage I mucinous borderline ovarian tumors were elevated via the interstitial infiltration of leukocytes and that precancerous lesions are associated with a cancerous glycosylation disorder in the process of inflammatory carcinogenesis, the CA19-9 level may be considered a suitable biomarker for estimating drug susceptibility

Smartphone-based two-wheeled self-balancing vehicles rider assistant

This paper presents an approach to a driver assistant system for a two-wheeled self-balancing mobility vehicles in particular for a Segway. The approach is aimed for the readily available mobile devices, which become a part of our daily life such as a smartphone or a tablet. If a mobile device is well-positioned on a mobility vehicle, its front and rear cameras can be utilized as sensors to capture the ride related information about the rider's intention(s) and the interaction of the rider with the environment. In addition, attached to the handle bar of the mobility vehicle, this mobile device can be used to alert the driver using the motion and location sensor as well as cameras and gather ride characteristics. In this study, we describe a context-aware system that continuously observes both the rider and the dynamical characteristics of the ride and provides alerts to the rider anticipating the hazards, collision, the route of the other public road users, and the stability of the current ride characteristics

Experimental Study under Real-World Conditions to Develop Fault Detection for Automated Vehicles

Abstract Automated vehicles can contribute to the improvement of transportation through their high capacity, increased safety, low emission and high efficiency. However, unstable conditions of automated mobile systems, which include automated vehicles and mobile robots) can cause serious problems, andthus, automated mobile system requiresto be highly reliable. The objective of this research is to develop on analgorith mfor detection faults (unstable condition) in an automated mobile system and to improve the overall reliability of this system. In this study, we in itially stored and updated a few patterns of data constellations under normal and unstable conditions for fault identification through real-world experiments. Multiple experiments were performed in a public urban area (with course distance per set beingapproximately1.1[km]), where several pedestrians, bicycles, and other robots were also present. The method used for detecting faults utilizes Mahalanobis distance, correlat ion coefficient, and linearization in order to enhance the accuracy of detecting faults;further, because real-world experimental conditions vary frequently,it is essential for the proposed method to be robust undervarious conditions. The ma in feature of this study is that it involves the use of experimental results obtained under real-world conditions, to develop a fault detection algorithm and evaluate its validity. In addition, simu lations were performed using the real-world experimental data, wh ich includes newly logged experimental data after the algorithm was developed in order to evaluate the validity of the proposed algorithm. The simulat ion results show that the proposed algorithm detects faults accurately, thus, they prove its validity

Context-Based Rider Assistant System for Two Wheeled Self-Balancing Vehicles

Personal mobility devises become more and more popular last years. Gyroscooters, two wheeled self-balancing vehicles, wheelchair, bikes, and scooters help people to solve the first and last mile problems in big cities. To help people with navigation and to increase their safety the intelligent rider assistant systems can be utilized that are used the rider personal smartphone to form the context and provide the rider with the recommendations. We understand the context as any information that characterize current situation. So, the context represents the model of current situation. We assume that rider mounts personal smartphone that allows it to track the rider face using the front-facing camera. Modern smartphones allow to track current situation using such sensors as: GPS / GLONASS, accelerometer, gyroscope, magnetometer, microphone, and video cameras. The proposed rider assistant system uses these sensors to capture the context information about the rider and the vehicle and generates context-oriented recommendations. The proposed system is aimed at dangerous situation detection for the rider, we are considering two dangerous situations: drowsiness and distraction. Using the computer vision methods, we determine parameters of the rider face (eyes, nose, mouth, head pith and rotation angles) and based on analysis of this parameters detect the dangerous situations. The paper presents a comprehensive related work analysis in the topic of intelligent driver assistant systems and recommendation generation, an approach to dangerous situation detection and recommendation generation is proposed, and evaluation of the distraction dangerous state determination for personal mobility device riders

EGUIDE project and treatment guidelines

Aim: Although treatment guidelines for pharmacological therapy for schizophrenia and major depressive disorder have been issued by the Japanese Societies of Neuropsychopharmacology and Mood Disorders, these guidelines have not been well applied by psychiatrists throughout the nation. To address this issue, we developed the ‘Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE)’ integrated education programs for psychiatrists to disseminate the clinical guidelines. Additionally, we conducted a systematic efficacy evaluation of the programs. Methods: Four hundred thirteen out of 461 psychiatrists attended two 1‐day educational programs based on the treatment guidelines for schizophrenia and major depressive disorder from October 2016 to March 2018. We measured the participants’ clinical knowledge of the treatment guidelines using self‐completed questionnaires administered before and after the program to assess the effectiveness of the programs for improving knowledge. We also examined the relation between the participants’ demographics and their clinical knowledge scores. Results: The clinical knowledge scores for both guidelines were significantly improved after the program. There was no correlation between clinical knowledge and participant demographics for the program on schizophrenia; however, a weak positive correlation was found between clinical knowledge and the years of professional experience for the program on major depressive disorder. Conclusion: Our results provide evidence that educational programs on the clinical practices recommended in guidelines for schizophrenia and major depressive disorder might effectively improve participants’ clinical knowledge of the guidelines. These data are encouraging to facilitate the standardization of clinical practices for psychiatric disorders

A COOPERATIVE ASSISTANCE SYSTEM BETWEEN VEHICLES FOR ELDERLY DRIVERS

This paper proposes a new concept of elderly driver assistance systems, which performs the assistance by cooperative driving between two vehicles, and describes some experiments with elderly drivers. The assistance consists of one vehicle driven by an elderly driver called a guest vehicle and the other driven by a assisting driver called a host vehicle, and the host vehicle assists or escorts the guest vehicle through the inter-vehicle communications. The functions of the systems installed on a single-seat electric vehicle are highly evaluated by subjects of elderly drivers in virtual streets on a test track

Guidance System to Target Spot for Charging by Communications

This paper is concerned with a docking assistance system on a charging station for electric vehicles (EV). An EV is automatically guided to a target docking spot for charging with high precision. This paper proposes a new concept of a vehicle guidance system for approaching a target spot for charging, which is an extension of the driver assistance by the cooperation between vehicles and infrastructure, and aims at an intelligent charging station. The system proposed here contributes to make it easy to approach a target spot for charging. Simulation studies and experiments are conducted to show the feasibility of the system proposed. In the experiment, a vehicle was guided by the proposed system that measures the position and the heading of a guided vehicle to indicate it the appropriate steering and the velocity to its goal