Development of Voice Control Algorithm for Robotic Wheelchair Using MIN and LSTM Models

Abstract

In this work, we developed and implemented a voice control algorithm to steer smart robotic wheelchairs (SRW) using the neural network technique. This technique used a network in network (NIN) and long short-term memory (LSTM) structure integrated with a built-in voice recognition algorithm. An Android Smartphone application was designed and configured with the proposed method. A Wi-Fi hotspot was used to connect the software and hardware components of the system in an offline mode. To operate and guide SRW, the design technique proposed employing five voice commands (yes, no, left, right, no, and stop) via the Raspberry Pi and DC motors. Ten native Arabic speakers trained and validated an English speech corpus to determine the method’s overall effectiveness. The design method of SRW was evaluated in both indoor and outdoor environments in order to determine its time response and performance. The results showed that the accuracy rate for the system reached 98.2% for the five-voice commends in classifying voices accurately. Another interesting finding from the real-time test was that the root-mean-square deviation (RMSD) for indoor/outdoor maneuvering nodes was 2.2*10–5 (for latitude), while that for longitude coordinates was a whopping 2.4*10–5 (for latitude).