Volting, a Novel Dancing Wheelchair with Augmented Mobility: Pushing Lateral Inclinations.

Wheelchair users are often perceived as someone ill and who will be limited in performing daily activities. This paradigm can be changed if instead to focus on limits, we start to think about the new possibilities that could be explored from their current mobility and technology. We present a novel dancing wheelchair with augmented mobility named Volting. Our novel wheelchair was designed to tilt the seat laterally up to 14°. This inclination is performed proportionally to the inclination of the user by a mechanism based on passive suspensions. Our system was analyzed as a double inverted pendulum and a mathematical model was developed using Euler-Lagrange equations. This analysis was used to calculate the ideal stiffness. Thus, we performed experiments with three distinct stiffness values and varying the weight of participants to analyze the behavior of our mechanism. Our results show that lateral inclinations in our wheelchair can be unstable, low sensitivity or linear tendency. The latter behavior, which is the most appropriate, was obtained using the suspension whose stiffness was close to the ideal value, thus validating our mathematical approach. Moreover, this behavior was maintained even if the user weight varies up to 10kg above the estimated value, ensuring a good performance for varying morphologies. Finally, our device was tested by a professional wheelchair dancer who shows the new possibilities of Volting in terms of mobility.

WISP, Wearable Inertial Sensor for Online Wheelchair Propulsion Detection.

Manual wheelchair dance is an artistic recreational and sport activity for people with disabilities that is becoming more and more popular. It has been reported that a significant part of the dance is dedicated to propulsion. Furthermore, wheelchair dance professionals such as Gladys Foggea highlight the need for monitoring the quantity and timing of propulsions for assessment and learning. This study addresses these needs by proposing a wearable system based on inertial sensors capable of detecting and characterizing propulsion gestures. We called the system WISP. Within our initial configuration, three inertial sensors were placed on the hands and the back. Two machine learning classifiers were used for online bilateral recognition of basic propulsion gestures (forward, backward, and dance). Then, a conditional block was implemented to rebuild eight specific propulsion gestures. Online paradigm is intended for real-time assessment applications using sliding window method. Thus, we evaluate the accuracy of the classifiers in two configurations: "three-sensor" and "two-sensor". Results showed that when using "two-sensor" configuration, it was possible to recognize the propulsion gestures with an accuracy of 90.28%. Finally, the system allows to quantify the propulsions and measure their timing in a manual wheelchair dance choreography, showing its possible applications in the teaching of dance.

SenseJoy, a pluggable solution for assessing user behavior during powered wheelchair driving tasks.

BACKGROUND: The complex task of Electric Powered Wheelchairs (EPW) prescription relies mainly on personal experience and subjective observations despite standardized processes and protocols. The most informative measurements come from joystick monitoring, but recording direct joystick outputs require to disassemble the joystick. We propose a new solution called "SenseJoy" that is easy to plug on a joystick and is suitable to characterize the driver behavior by estimating the joystick command. METHODS: SenseJoy is a pluggable system embedded on EPW built with a 3D accelerometer and a 2D gyrometer placed within the joystick and another 3D accelerometer located at the basis of the joystick. Data is sampled at 39 Hz and processed offline. First, SenseJoy sensitivity is assessed on wheelchair driving tasks performed by a group of 8 drivers (31 ± 8 years old, including one driver with left hemiplegia, one with cerebral palsy) in a lab environment. Direct joystick measurements are compared with SenseJoy estimations in different driving exercises. A second group of 5 drivers is recorded in the ecological context of a rehabilitation center (41 ± 10 years old, with two tetraplegic drivers, one tetraplegic driver with cognitive disorder, one driver post-stroke, one driver with right hemiplegia). The measurements from all groups of drivers are evaluated with an unsupervised statistical analysis, to estimate driving profile clusters. RESULTS: The SenseJoy is able to measure the EPW joystick inclination angles with a resolution of 1.31% and 1.23% in backward/forward and left/right directions respectively. A statistical validation ensures that the classical joystick-based indicators are equivalent when acquired with the SenseJoy or with a direct joystick output connection. Using an unsupervised methodology, based on a similarity matrix between subjects, it is possible to characterize the driver profile from real data. CONCLUSION: SenseJoy is a pluggable system for assessing the joystick controls during EPW driving tasks. This system can be plugged on any EPW equipped with a joystick control interface. We demonstrate that it correctly estimates the performance indicators and it is able to characterize driving profile. The system is suitable and efficient to assist therapists in their recommendation, by providing objective measures with a fast installation process.

Influence of extracellular oscillations on neural communication: a computational perspective.

Neural communication generates oscillations of electric potential in the extracellular medium. In feedback, these oscillations affect the electrochemical processes within the neurons, influencing the timing and the number of action potentials. It is unclear whether this influence should be considered only as noise or it has some functional role in neural communication. Through computer simulations we investigated the effect of various sinusoidal extracellular oscillations on the timing and number of action potentials. Each simulation is based on a multicompartment model of a single neuron, which is stimulated through spatially distributed synaptic activations. A thorough analysis is conducted on a large number of simulations with different models of CA3 and CA1 pyramidal neurons which are modeled using realistic morphologies and active ion conductances. We demonstrated that the influence of the weak extracellular oscillations, which are commonly present in the brain, is rather stochastic and modest. We found that the stronger fields, which are spontaneously present in the brain only in some particular cases (e.g., during seizures) or that can be induced externally, could significantly modulate spike timings.

Fuzzy Adjustment Control in Command Evaluation Platform.

OBJECTIVE: For motor handicapped persons, the use of computer interface devices could be a challenging task. Two research venues have been explored to address the usability problem for handicapped users of computer systems. In the first, the focus is on development of new peripheral devices to accommodate the needs of such users. The second venue, which is the focus of this article, addresses the development of efficient rehabilitation algorithms that can adapt existing interface devices to commands from handicapped users. The main goal of this article is to analyze the existing software adaptation platform PLatform to Evaluate ICT for Assistance with a focus on evaluating and enhancing the use of pointing devices for increased adaptation to interface commands. SUBJECTS AND METHODS: In the first part of this article, a new quantitative analysis approach is presented, which is useful to occupational therapists in choosing among existing information and communication technologies. We define quantification indicators to be used in the analysis of a person's movements. The proposed indicators would be classified in two categories: Task indicators and adaption indicators. In the second part of the article, we present a fuzzy rehabilitation complementary module that adapts the user command without the need to adjust the interface device. The subject is a quadriplegic 12-year-old boy. The test was conducted using five attempts after a brief description and a small demonstration of the experiment. The test was rerun the second day for another five attempts. RESULTS: The subject could not command the joystick in the left direction without a customized device. With a customized device, he was able to control properly the mouse cursor on the screen. CONCLUSIONS: The adaptation technique used in this work helped the subject to properly control the computer pointing device. The evaluation technique helped assess the subject and give the proper parameters to the adaptation algorithm.

Nonlinear and nonstationary framework for feature extraction and classification of motor imagery.

In this work we investigate a nonlinear approach for feature extraction of Electroencephalogram (EEG) signals in order to classify motor imagery for Brain Computer Interface (BCI). This approach is based on the Empirical Mode Decomposition (EMD) and band power (BP). The EMD method is a data-driven technique to analyze non-stationary and nonlinear signals. It generates a set of stationary time series called Intrinsic Mode Functions (IMF) to represent the original data. These IMFs are analyzed with the power spectral density (PSD) to study the active frequency range correspond to the motor imagery for each subject. Then, the band power is computed within a certain frequency range in the channels. Finally, the data is reconstructed with only the specific IMFs and then the band power is employed on the new database. The classification of motor imagery was performed by using two classifiers, Linear Discriminant Analysis (LDA) and Hidden Markov Models (HMMs). The results obtained show that the EMD method allows the most reliable features to be extracted from EEG and that the classification rate obtained is higher and better than using only the direct BP approach.