Synthesis, dynamic modeling, prototyping and control of a handheld rotational inertia generator.

This paper explores the design and experimental validation of a three-degree-of-freedom variable inertia generator. An inertia generator is a handheld haptic device that renders a prescribed inertia. In the mechanism proposed in this paper, three-dimensional torque feedback is achieved by accelerating three pairs of flywheels mounted on orthogonal axes. While the primary objective of this work is to design an inertia generator, this study also includes developing other functionalities for the device that exploit its torque generation capabilities. These include the ability to generate a predefined torque profile and to simulate a viscous environment through damping, which are both utilized to assess the device's performance. The device proved to accurately render the necessary torques for every functionality while presenting some limitations for damping and rendering an inertia smaller than the device's inherent inertia.

Multiple Cylinder Extraction from Organized Point Clouds.

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.

Analysis and synthesis of assistive tools for insertion tasks.

This article studies two types of assembly tasks, namely snap-fit insertions and press-fit hose insertions. Experimental data and theoretical modelling of a snap-fit assembly are used to design a tool that can perform the snap-fit task effectively. The design process of the tool is presented and experimental tests developed to validate its effectiveness are described. Hose insertion experiments are then performed and the results are analyzed in order to develop strategies for the effective insertion of press-fit components in assembly tasks. A motion primitive strategy is first explored, followed by a vibration oriented strategy. Finally, a video demonstrating the experiments accompanies this paper.

Adding Haptic Feedback to Virtual Environments With a Cable-Driven Robot Improves Upper Limb Spatio-Temporal Parameters During a Manual Handling Task.

Physical interactions within virtual environments are often limited to visual information within a restricted workspace. A new system exploiting a cable-driven parallel robot to combine visual and haptic information related to environmental physical constraints (e.g. shelving, object weight) was developed. The aim of this study was to evaluate the impact on user movement patterns of adding haptic feedback in a virtual environment with this robot. Twelve healthy participants executed a manual handling task under three conditions: 1) in a virtual environment with haptic feedback; 2) in a virtual environment without haptic feedback; 3) in a real physical environment. Temporal parameters (movement time, peak velocity, movement smoothness, time to maximum flexion, time to peak wrist velocity) and spatial parameters of movement (maximum trunk flexion, range of motion of the trunk, length of the trajectory, index of curvature and maximum clearance from the shelf) were analysed during the reaching, lowering and lifting phases. Our results suggest that adding haptic feedback improves spatial parameters of movement to better respect the environmental constraints. However, the visual information presented in the virtual environment through the head mounted display appears to have an impact on temporal parameters of movement leading to greater movement time. Taken together, our results suggest that a cable-driven robot can be a promising device to provide a more ecological context during complex tasks in virtual reality.

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning.

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

Real-Time Hand Motion Recognition Using sEMG Patterns Classification.

Increasing performance while decreasing the cost of sEMG prostheses is an important milestone in rehabilitation engineering. The different types of prosthetic hands that are currently available to patients worldwide can benefit from more effective and intuitive control. This paper presents a real-time approach to classify finger motions based on surface electromyography (sEMG) signals. A multichannel signal acquisition platform implemented using components off the shelf is used to record forearm sEMG signals from 7 channels. sEMG pattern classification is performed in real time, using a Linear Discriminant Analysis approach. Thirteen hand motions can be successfully identified with an accuracy of up to 95.8% and of 92.7% on average for 8 participants, with an updated prediction every 192 ms.

A Multimodal Adaptive Wireless Control Interface for People With Upper-Body Disabilities.

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 2.9 cm, a maximum power consumption of 31 mW, and provides angular precision of 1. 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 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.

Intuitive adaptive orientation control of assistive robots for people living with upper limb disabilities.

Robotic assistive devices enhance the autonomy of individuals living with physical disabilities in their day-to-day life. Although the first priority for such devices is safety, they must also be intuitive and efficient from an engineering point of view in order to be adopted by a broad range of users. This is especially true for assistive robotic arms, as they are used for the complex control tasks of daily living. One challenge in the control of such assistive robots is the management of the end-effector orientation which is not always intuitive for the human operator, especially for neophytes. This paper presents a novel orientation control algorithm designed for robotic arms in the context of human-robot interaction. This work aims at making the control of the robot's orientation easier and more intuitive for the user, in particular, individuals living with upper limb disabilities. The performance and intuitiveness of the proposed orientation control algorithm is assessed through two experiments with 25 able-bodied subjects and shown to significantly improve on both aspects.

Development and Experimental Validation of a Haptic Compass Based on Asymmetric Torque Stimuli.

This paper presents the design, control, and experimental validation of a haptic compass, designed as a guiding device for all environments. The proposed device uses the principle of asymmetric torques. Its design is based on a direct drive motor and a pre-calibrated open-loop control, which allows the generation of stimuli in a wide range of frequencies. User studies are presented and show optimum effectiveness in the frequency range 5-15 Hz and for torques over 40 mNm. The use of a haptic feedback proportional to the angle error is then shown to significantly improve the results. An experimental validation by a group of' subjects with the portable device using these stimuli is reported. The results show that all subjects met all route objectives with small lateral deviations (avg. 0.39 m).

Wireless sEMG-Based Body-Machine Interface for Assistive Technology Devices.

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.

On the development of a walking rehabilitation device with a large workspace.

Rehabilitation therapy aiming at helping a person to regain or improve the ability to walk is a labour-intensive activity. In this context, the patient is often limited to very restricted walking spaces. This paper presents a device that can support the weight of a person walking freely over a large workspace. The device is based on an overhead gantry mechanism combined with a cable routing that decouples the vertical motion from the horizontal displacements of the person. The mechanical architecture is first presented and it is shown that the principle can be applied to the design of a completely passive device in which the portion of the weight to be supported can be adjusted. A simplified dynamic model is also derived in order to highlight the characteristics of the device. A powered version of the device is then discussed. Finally, a prototype of a passive device built at full scale is presented and discussed. A video accompanying the paper illustrates the experimental tests underway with the prototype.

A first semimanual device for clinical intramuscular repetitive cell injections.

Intramuscular cell transplantation in humans requires so far meticulous repetitive cell injections. Performed percutaneously with syringes operated manually, the procedure is very time consuming and requires a lot of concentration to deliver the cells exactly in the required region. This becomes impractical and inaccurate for large volumes of muscle. In order to accelerate this task, to render it more precise, and to perform injections more reproducible in large volumes of muscle, we developed a specific semimanual device for intramuscular repetitive cell injections. Our prototype delivers very small quantities of cell suspension, homogeneously throughout several needles, from a container in the device. It was designed in order to deliver the cells as best as possible only in a given subcutaneous region (in our case, skeletal muscles accessible from the surface), avoiding wasting in skin and hypodermis. The device was tested in monkeys by performing intramuscular allotransplantations of beta-galactosidase-labeled myoblasts. During transplantations, it was more ergonomic and considerably faster than manually operated syringes, facilitating the cell graft in whole limb muscles. Biopsies of the myoblast-injected muscles 1 month later showed abundant beta-galactosidase-positive myofibers with homogeneous distribution through the biopsy sections. This is the first device specifically designed for the needs of intramuscular cell transplantation in a clinical context.

An electrohydraulic actuated ankle foot orthosis to generate force fields and to test proprioceptive reflexes during human walking.

The control of human walking can be temporarily modified by applying forces to the leg. To study the neural mechanisms underlying this adaptive capacity, a device delivering controlled forces and high-velocity displacements to the ankle was designed. A new solution, involving a closed circuit hydraulic system composed of two cylinders (master-slave) mutually connected by hoses and controlled by an electric motor was preferred over classical mechanical/electrical approaches. The slave cylinder delivers desired torques to the ankle using a light weight, custom-designed ankle-foot orthosis. This electrohydraulic orthosis (EHO) can produce several types of force fields during walking, including constant, position-dependent, and phase-dependent. With phase-dependent force fields, active torque cancellation maintains low-residual torques ( < or = 1.85 Nm root mean square) outside of the zone of force application for walking speeds ranging from 0.2 to 4.5 km/h. Rapid ankle stretches/unloads ( > 200 degrees/s) can also be produced alone or during force field application, and elicited proprioceptive reflexes in ankle muscles. In conclusion, the EHO is capable of delivering controlled force fields and of activating proprioceptive reflexes during human walking. It will provide the flexibility needed to test the adaptability of healthy and pathological gait control, and to address some of its underlying neural mechanisms.