Sign language is intentionally designed to allow deaf and dumb communities to convey messages and to connect with society. Unfortunately, learning and practicing sign language is not common among society; hence, this study developed a sign language recognition prototype using the Leap Motion Controller (LMC). Many existing studies have proposed methods for incomplete sign language recognition, whereas this study aimed for full American Sign Language (ASL) recognition, which consists of 26 letters and 10 digits. Most of the ASL letters are static (no movement), but certain ASL letters are dynamic (they require certain movements). Thus, this study also aimed to extract features from finger and hand motions to differentiate between the static and dynamic gestures. The experimental results revealed that the sign language recognition rates for the 26 letters using a support vector machine (SVM) and a deep neural network (DNN) are 80.30% and 93.81%, respectively. Meanwhile, the recognition rates for a combination of 26 letters and 10 digits are slightly lower, approximately 72.79% for the SVM and 88.79% for the DNN. As a result, the sign language recognition system has great potential for reducing the gap between deaf and dumb communities and others. The proposed prototype could also serve as an interpreter for the deaf and dumb in everyday life in service sectors, such as at the bank or post office.

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that “fuses” six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.

In recent decades, employees who worked in office suffered from bone diseases and muscle stress, mainly due to improper sitting posture. This paper is proposed to develop a novel body equilibrium correction to meet solve such issue. The system consists of four modules. A motion sensor module is placed on the center of the user’s chest to measure the sitting orientation. Moreover, an electromyography (EMG) module is located at the both side of the shoulders to measure the user’s muscle tension as incorrect sitting position over a long period of time tends to increase the pain and tense at the shoulders. Thus, it is reliable to serve as references in accordance to the sitting orientation. Both sensor modules transmit the raw data to a processing module. This module integrates the raw data under a packet and further transmits the data packet to a mobile device. A mobile application is implemented to classify the correctness of sitting position. In case the incorrect position is detected, alert is triggered to user by the vibrator motor and light emitting diodes (LEDs) installed along with the system. The proposed system is able to measure and detect any abnormal sitting positon with average true accuracy of 98% with low-cost implementation.