Sign language recognition using Kinect sensor based on color stream and skeleton points

This paper presents a sign language recognition system based on color stream and skeleton points. Several approaches have been established to address sign language recognition problems. However, most of the previous approaches still have poor recognition accuracy. The proposed approach uses Kinect sensor based on color stream and skeleton points from the depth stream to improved recognition accuracy. Techniques within this approach use hand trajectories and hand shapes in combating sign recognition challenges. Therefore, for a particular sign a representative feature vector is extracted, which consists of hand trajectories and hand shapes. A sparse dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD) is applied to obtain a discriminative dictionary. Based on that, the system was further developed to a new classification approach for better results. The proposed system was fairly evaluated based on 21 sign words including one-handed signs and two-handed signs. It was observed that the proposed system gets high recognition accuracy of 98.25%, and obtained an average accuracy of 95.34% for signer independent recognition. Keywords: Sign language, Color stream, Skeleton points, Kinect sensor, Discriminative dictionary

Real-time Complex Hand Gestures Recognition Based on MultiDimensional Features

Gesture recognition is broadly utilized within the field of sensing. There are basically three gesture recognition methods based on computer vision, depth sensor and motion sensor. Motion sensor-based gesture recognition has few input data, fast speed, and direct access to threedimensional information of the hand. The advantages of traditional motion sensor-based gesture recognition have gradually become a current research hotspot. The essence of traditional motion sensor-based gesture recognition is a pattern recognition problem, and its accuracy depends heavily on the feature dataset extracted from prior experience. Unlike traditional pattern recognition methods, deep learning can be used to a large extent, reducing the workload of artificial heuristic extraction of features. In order to solve the problems of traditional pattern recognition, this paper proposes a real-time recognition method of multifeature gestures based on a long short-term memory network (LSTM), which is verified by sufficient experiments. The method first defines a gesture library of five (5) basic gestures and seven (7) complex gestures. Based on the kinematic characteristics of the hand posture, the angle features and displacement features are further extracted, and then short-time Fourier transform (SFTF) is used. The frequency domain features of sensor data are extracted, and the three features are input into the deep neural network LSTM to train, classify and recognize the collected gestures. At the same time, to verify the effectiveness of the proposed method, a selfdesigned handheld experience stick is collected. The gesture data of six (6) volunteers is used as an experimental data set. The collected experimental results show that the proposed recognition method has a recognition accuracy of 93.50% for basic and complex gestures. Compared with other methods, the recognition accuracy has increased by nearly 2%

Abstracts of Tanzania Health Summit 2020

This book contains the abstracts of the papers/posters presented at the Tanzania Health Summit 2020 (THS-2020) Organized by the Ministry of Health Community Development, Gender, Elderly and Children (MoHCDGEC); President Office Regional Administration and Local Government (PORALG); Ministry of Health, Social Welfare, Elderly, Gender, and Children Zanzibar; Association of Private Health Facilities in Tanzania (APHFTA); National Muslim Council of Tanzania (BAKWATA); Christian Social Services Commission (CSSC); & Tindwa Medical and Health Services (TMHS) held on 25–26 November 2020. The Tanzania Health Summit is the annual largest healthcare platform in Tanzania that attracts more than 1000 participants, national and international experts, from policymakers, health researchers, public health professionals, health insurers, medical doctors, nurses, pharmacists, private health investors, supply chain experts, and the civil society. During the three-day summit, stakeholders and decision-makers from every field in healthcare work together to find solutions to the country’s and regional health challenges and set the agenda for a healthier future. Summit Title: Tanzania Health SummitSummit Acronym: THS-2020Summit Date: 25–26 November 2020Summit Location: St. Gasper Hotel and Conference Centre in Dodoma, TanzaniaSummit Organizers: Ministry of Health Community Development, Gender, Elderly and Children (MoHCDGEC); President Office Regional Administration and Local Government (PORALG); Ministry of Health, Social Welfare, Elderly, Gender and Children Zanzibar; Association of Private Health Facilities in Tanzania (APHFTA); National Muslim Council of Tanzania (BAKWATA); Christian Social Services Commission (CSSC); & Tindwa Medical and Health Services (TMHS)