Recent results on the development of a navigation system for a smart wheelchair are presented in this paper. In order to reduce the development cost, a modular solution is designed by using commercial and low cost dev...Recent results on the development of a navigation system for a smart wheelchair are presented in this paper. In order to reduce the development cost, a modular solution is designed by using commercial and low cost devices. The functionalities of the tracking control system are described. Experimental results of the proposed assistive system are also presented and discussed.展开更多
A navigation method based on the partially observable markov decision process （POMDP） for smart wheelchairs in uncertain environments is presented in this paper. The design key factors for the navigation system of a...A navigation method based on the partially observable markov decision process （POMDP） for smart wheelchairs in uncertain environments is presented in this paper. The design key factors for the navigation system of a smart wheelchair are discussed. A kinematics model of the smart wheelchair is given, and the model and principle of POMDP are introduced. In order to respond in uncertain local environments, a novel navigation methodology based on POMDP using the sensors perception and the user＇s joystick input is presented. The state space, the action set, the observations and the sensor fusion of the navigation method are given in detail, and the optimal policy of the POMDP model is proposed. Experimental results demonstrate the feasibility of this navigation method. Analysis is also conducted to investigate performance evaluation, advantages of the approach and potential generalization of this paper.展开更多
A smart wheelchair provides mobility assistance to persons with motor disabilities by processing sensory inputs from the person.This involves accurately collecting inputs from the user during various movement activiti...A smart wheelchair provides mobility assistance to persons with motor disabilities by processing sensory inputs from the person.This involves accurately collecting inputs from the user during various movement activities and using them to determine their intended motion.These smart wheelchairs work by collecting brain signals in the form of electroencephalography(EEG)signals and by processing them into a quantized format to provide movement assistance to people.Such systems can be referred to as brain-computer interface(BCI)systems that work with EEG signals.Acquiring data from human beings in the form of brain signals through EEG,along with processing of those signals and ensuring the correctness of actions instigated by those brain signals involve a huge amount of data.In this work,we carried out an experiment by taking 100 human subjects and recording their brain signals using a NeuroMax device.Typical wheelchairs are constrained by design as the motion of those is limited either by manual operation or controlled by haptic sensors and actuators.The main objective in this work was to design a wheelchair with better usability and control using machine learning-based knowledge,which is typically a data-driven approach.However,the proposed approach was designed to take inputs from human gestures and brain sensory activities to provide better usability to the wheelchair.The attention meditation cost–benefit analysis(AMCBA)proposed in this paper aims to reduce the risk of inappropriate results and improve performance by considering various cost-benefit parameters.The said classifier aims to improve the quality of emotion recognition by filtering features from EEG signals using methods of feature selection.The operation of the proposed method is described in two steps:in the first step,we assign weights to different channels for the extraction of spatial and temporal information from human behavior.The second step presents the cost-benefit model to improve the accuracy to help in decision-making.Moreover,we tried to assess the performance of the wheelchair for various assumptions and technical specifications.Finally,this study achieves improved performance in the most difficult circumstances to provide a better experience to persons with immobility.展开更多
In order to assist physically handicapped persons in their movements,we developed an embedded isolated word speech recognition system(ASR)applied to voice control of smart wheelchairs.However,in spite of the existence...In order to assist physically handicapped persons in their movements,we developed an embedded isolated word speech recognition system(ASR)applied to voice control of smart wheelchairs.However,in spite of the existence in the industrial market of several kinds of electric wheelchairs,the problem remains the need to manually control this device by hand via joystick;which limits their use especially by people with severe disabilities.Thus,a significant number of disabled people cannot use a standard electric wheelchair or drive it with difficulty.The proposed solution is to use the voice to control and drive the wheelchair instead of classical joysticks.The intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors,a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP card.The ASR architecture consists of two main modules.The first one is the speech parameterization module(features extraction)and the second module is the classifier which identifies the speech and generates the control word to motors power unit.The training and recognition phases are based on Hidden Markov Models(HMM),K-means,Baum-Welch and Viterbi algorithms.The database consists of 39 isolated speaker words(13 words pronounced 3 times under different environments and conditions).The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP kit.The results and experiments obtained gave promising recognition ratio and accuracy around 99%in clean environment.However,the system accuracy decreases considerably in noisy environments,especially for SNR values below 5 dB(in street:78%,in factory:52%).展开更多
文摘Recent results on the development of a navigation system for a smart wheelchair are presented in this paper. In order to reduce the development cost, a modular solution is designed by using commercial and low cost devices. The functionalities of the tracking control system are described. Experimental results of the proposed assistive system are also presented and discussed.
文摘A navigation method based on the partially observable markov decision process （POMDP） for smart wheelchairs in uncertain environments is presented in this paper. The design key factors for the navigation system of a smart wheelchair are discussed. A kinematics model of the smart wheelchair is given, and the model and principle of POMDP are introduced. In order to respond in uncertain local environments, a novel navigation methodology based on POMDP using the sensors perception and the user＇s joystick input is presented. The state space, the action set, the observations and the sensor fusion of the navigation method are given in detail, and the optimal policy of the POMDP model is proposed. Experimental results demonstrate the feasibility of this navigation method. Analysis is also conducted to investigate performance evaluation, advantages of the approach and potential generalization of this paper.
文摘A smart wheelchair provides mobility assistance to persons with motor disabilities by processing sensory inputs from the person.This involves accurately collecting inputs from the user during various movement activities and using them to determine their intended motion.These smart wheelchairs work by collecting brain signals in the form of electroencephalography(EEG)signals and by processing them into a quantized format to provide movement assistance to people.Such systems can be referred to as brain-computer interface(BCI)systems that work with EEG signals.Acquiring data from human beings in the form of brain signals through EEG,along with processing of those signals and ensuring the correctness of actions instigated by those brain signals involve a huge amount of data.In this work,we carried out an experiment by taking 100 human subjects and recording their brain signals using a NeuroMax device.Typical wheelchairs are constrained by design as the motion of those is limited either by manual operation or controlled by haptic sensors and actuators.The main objective in this work was to design a wheelchair with better usability and control using machine learning-based knowledge,which is typically a data-driven approach.However,the proposed approach was designed to take inputs from human gestures and brain sensory activities to provide better usability to the wheelchair.The attention meditation cost–benefit analysis(AMCBA)proposed in this paper aims to reduce the risk of inappropriate results and improve performance by considering various cost-benefit parameters.The said classifier aims to improve the quality of emotion recognition by filtering features from EEG signals using methods of feature selection.The operation of the proposed method is described in two steps:in the first step,we assign weights to different channels for the extraction of spatial and temporal information from human behavior.The second step presents the cost-benefit model to improve the accuracy to help in decision-making.Moreover,we tried to assess the performance of the wheelchair for various assumptions and technical specifications.Finally,this study achieves improved performance in the most difficult circumstances to provide a better experience to persons with immobility.
文摘In order to assist physically handicapped persons in their movements,we developed an embedded isolated word speech recognition system(ASR)applied to voice control of smart wheelchairs.However,in spite of the existence in the industrial market of several kinds of electric wheelchairs,the problem remains the need to manually control this device by hand via joystick;which limits their use especially by people with severe disabilities.Thus,a significant number of disabled people cannot use a standard electric wheelchair or drive it with difficulty.The proposed solution is to use the voice to control and drive the wheelchair instead of classical joysticks.The intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors,a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP card.The ASR architecture consists of two main modules.The first one is the speech parameterization module(features extraction)and the second module is the classifier which identifies the speech and generates the control word to motors power unit.The training and recognition phases are based on Hidden Markov Models(HMM),K-means,Baum-Welch and Viterbi algorithms.The database consists of 39 isolated speaker words(13 words pronounced 3 times under different environments and conditions).The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP kit.The results and experiments obtained gave promising recognition ratio and accuracy around 99%in clean environment.However,the system accuracy decreases considerably in noisy environments,especially for SNR values below 5 dB(in street:78%,in factory:52%).
