Search

This paper describes a multimodal body–machine interface (BoMI) to help individuals with upper-limb disabilities using advanced assistive technologies, such as robotic arms. The proposed system uses a wearable and wireless body sensor network (WBSN) supporting up to six sensor nodes to measure the natural upper-body gesture of the users and translate it into control commands. Natural gesture of the head and upper-body parts, as well as muscular activity, are measured using inertial measurement units (IMUs) and surface electromyography (sEMG) using custom-designed multimodal wireless sensor nodes. An IMU sensing node is attached to a headset worn by the user. It has a size of 2.9 cm $\times$ 2.9 cm, a maximum power consumption of 31 mW, and provides angular precision of 1 $^\circ$. Multimodal patch sensor nodes, including both IMU and sEMG sensing modalities are placed over the user able-body parts to measure the motion and muscular activity. These nodes have a size of 2.5 cm $\times$ 4.0 cm and a maximum power consumption of 11 mW. The proposed BoMI runs on a Raspberry Pi. It can adapt to several types of users through different control scenarios using the head and shoulder motion, as well as muscular activity, and provides a power autonomy of up to 24 h. JACO, a 6-DoF assistive robotic arm, is used as a testbed to evaluate the performance of the proposed BoMI. Ten able-bodied subjects performed ADLs while operating the AT device, using the Test d’Évaluation des Membres Supérieurs de Personnes Âgées to evaluate and compare the proposed BoMI with the conventional joystick controller. It is shown that the users can perform all tasks with the proposed BoMI, almost as fast as with the joystick controller, with only 30% time overhead on average, while being potentially more accessible to the upper-body disabled who cannot use the conventional joystick controller. Tests show that control performance with the proposed BoMI improved by up to 17% on average, after three trials. [ABSTRACT FROM AUTHOR]

During overground wheelchair propulsion, upper-body (UB) movement causes intracycle velocity variations that are neglected by current wheelchair simulators. This could affect the external validity of wheelchair propulsion on simulators. In this study, we investigated ways to incorporate these dynamics into the dynamic model (DM) reproduced by wheelchair simulators. We aimed to maximize the DM accuracy and minimize the number of required inputs. First, two DMs were presented: Model RL represented propulsion on a typical roller-based wheelchair simulator and model UB represented overground propulsion, modeling the UB as five rigid bodies. Then, three new DMs were presented: Model trunk (TR), model upper arm (UA), and model forearm (FA); these models simplified model UB by estimating the UB kinematics based on the acceleration of only one segment. For all DMs, wheelchair velocity prediction was tested overground at a self-selected velocity among 19 experienced manual wheelchair users with a spinal cord injury. UB kinematics was reconstructed based on personalized kinematic patterns recorded on a wheelchair simulator. Models UB and UA were the most accurate: they reduced the root-mean-square intracycle velocity prediction error from 0.044 m/s (RL) to 0.026 m/s (UB) and 0.024 m/s (UA), and reduced the velocity peak time prediction error from $-$27.7% (RL) to 1.7% (UB) and $-$7.3% (UA). Implementing model UA instead of model RL on a wheelchair simulator may improve the external validity of wheelchair propulsion on a simulator. [ABSTRACT FROM PUBLISHER]

Eighteen manual wheelchair users (MWUs) with spinal cord injury participated in a training session on a new manual wheelchair simulator with haptic biofeedback (HB). The training aimed to modify participants' mechanical effective force (MEF) along the push phase to achieve a target MEF pattern slightly more effective than their pre-training pattern. More HB was provided if the participants' achieved MEF pattern deviated from the target. Otherwise, less HB was provided. The deviation between the participants' achieved MEF and the target, as well as the mean achieved MEF, were computed before, during and after the training session. During the training, participants generally exceeded the target pattern at the beginning of the push cycle and achieved it towards the end. On average, participants also increased their mean MEF by up to 15.7% on the right side and 12.4% on the left side between the pre-training and training periods. Finally, eight participants could modify their MEF pattern towards the target in post-training. The simulator tested in this study represents a valuable tool for developing new wheelchair propulsion training programs. Haptic biofeedback also provides interesting potential for training MWUs to improve propulsion effectiveness. [ABSTRACT FROM AUTHOR]