The lack of high-quality in-game player data has been a challenge in affective computing and player experience research, as the currently available datasets do not fully capture the multimodal nature of the player-game interaction. The following paper addresses this issue by introducing a multimodal collection of video game player data: the FUNii Database. The database contains in-laboratory physiological, behavioral and subjective data from 190 players acquired while they played Ubisoft's Assassin's Creed: Unity and Assassin's Creed: Syndicate. The database also includes demographic data from the players and Fun Trace ratings, a unique tool for assessing player experience on a continuous scale following a game session. The database offers opportunities to study physio-behavioral responses in a gaming context, player profiling, player experience modeling or adaptation strategies for affective adaptive games.

Affective computing, with its potential to enhance human-computer interaction, is experiencing an expansion of its use in many areas such as health care and the gaming industry. One obstacle to its widespread adoption can be the high cost requirement for biofeedback. Indeed, typical laboratory setups are often expensive which makes them out of reach for many. This paper explores lower-cost alternatives to expensive laboratory solutions. Data from several recent studies totaling over 200 hours of physiological recordings are leveraged to compare high-end solutions to a lower cost one. Heart rate, electrodermal activity, facial action units, head movement, and eye movement - five of the most used bio-behavioural signals - have their respective higher and lower cost sensors compared. The resulting comparison illustrates that lower-cost solutions are not drop-in replacements. While a correlation of 0.62 between electrodermal activity readings was found, notable differences between reported heart rate readings over small timescales were also observed. Head tracking recordings shared similarity (0.51), but eye tracking did not (0.18). As for facial action units recognition, only those linked to smiling had significant correlation (around 0.48). These results should broaden the range of contexts in which biofeedback could be exploited. This aim may be fulfilled by informing the reader of the extent of lower cost solution applications.

Robotic devices can be leveraged to raise the abilities of humans to perform demanding and complex tasks with less effort. Although the first priority of such human-robot interaction (HRI) is safety, robotic devices must also be intuitive and efficient in order to be adopted by a broad range of users. One challenge in the control of such assistive robots is the management of the end-effector orientation, that 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 HRI. This paper aims at making the control of the robot's orientation easier and more intuitive for the user, both in the fields of rehabilitation (in particular individuals living with upper limb disabilities) and industrial robotics. The performance and intuitiveness of the proposed orientation control algorithm is assessed and improved through two experiments with a JACO assistive robot with 25 able-bodied subjects, an online survey with 117 respondents via the Amazon Mechanical Turk and through two experiments with a UR5 industrial robot with 12 able-bodied subjects.

This paper presents an anticipative robot kinematic limitation avoidance algorithm for collaborative robots. The main objective is to improve the performance and the intuitivity of physical human-robot interaction. Currently, in such interactions, the human user must focus on the task as well as on the robot configuration. Indeed, the user must pay a close attention to the robot in order to avoid limitations such as joint position limitations, singularities and collisions with the environment. The proposed anticipative algorithm aims at relieving the human user from having to deal with such limitations by automatically avoiding them while considering the user's intentions. The framework developed to manage several limitations occurring simultaneously in three-dimensional space is first presented. The algorithm is then presented and detailed for each individual limitation of a spatial RRR serial robot. Finally, experiments are performed in order to assess the performance of the algorithm.

Multimodal body-machine interfaces play an important role in providing severely impaired with interaction solutions that can adapt to their functional capacities [1]. This demonstration will allow the visitors to experience an intuitive and wearable control interface, designed for people with upper body disabilities, that translates head motion, shoulder elevation and surface electromyography into appropriate commands for controlling assistive devices, like robotic arms. Visitors will be invited to interact with JACO using the proposed interface, a 6 degree-of-freedom assistive robotic arm developed by Kinova Robotics, within a control task that will consist in moving items or stack objects at specific locations on a table.

This paper presents a new multimodal control interface for people living with upper-body disabilities based on a wearable wireless sensor network. The proposed body-machine interface is modular and can be easily adapted to the residual functional capacities (RFCs) of different users. A custom data fusion algorithm has been developed for emulating a joystick control using head motion measured with a lightweight wireless inertial sensor enclosed in a headset. The wearable network can include up to six modular sensor nodes which can be used simultaneously to read different RFCs including gesture and muscular activity, and translate them into commands. Sensor data fusion is performed inside the sensor nodes in order to free the wireless link and the base station, and decrease power consumption. Requirements of such an interface are established for people using powered-wheelchairs, and a proof of concept system is implemented and used to control an assistive robotic arm. It is shown that the performance of the system compares well to conventional control systems like the joystick controller, while being potentially more suitable for the severely disabled.

In the realm of surface electromyography (sEMG) gesture recognition, deep learning algorithms are seldom employed. This is due in part to the large quantity of data required for them to train on. Consequently, it would be prohibitively time consuming for a single user to generate a sufficient amount of data for training such algorithms. In this paper, two datasets of 18 and 17 able-bodied participants respectively are recorded using a low-cost, low-sampling rate (200Hz), 8-channel, consumer-grade, dry electrode sEMG device named Myo armband (Thalmic Labs). A convolutional neural network (CNN) is augmented using transfer learning techniques to leverage inter-user data from the first dataset and alleviate the data generation burden imposed on a single individual. The results show that the proposed classifier is robust and precise enough to guide a 6DoF robotic arm (in conjunction with orientation data) with the same speed and precision as with a joystick. Furthermore, the proposed CNN achieves an average accuracy of 97.81% on seven hand/wrist gestures on the 17 participants of the second dataset.

Many novel physical assistance devices are beginning to incorporate intelligent robotic systems and mechatronic components. In terms of a human-centered design it is crucial to assess the perceived subjective usability and acceptance of these systems. A questionnaire was thus designed to evaluate novel physically assisting devices in order to support developers in their design decisions as well as users during individualizing of their assistive devices. Two studies (m = 9, n2 = 21), using two different devices, were conducted to analyze objectivity, reliability, and validity. The results show an overall high internal consistency (Cronbach's α > 0.8), which indicates reliability and applicability of the QUEAD. Criterion validity was tested applying correlations with established objective measures for efficiency (time to task completion), effectivity (errors and collisions), and commitment (mean force). Construct validity was applied using a proposed model and correlations to verify convergence. The results show that the QUEAD is able to assess perceived usability and acceptance.

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.

This paper presents an anticipative robot kinematic limitation avoidance algorithm for collaborative robots. The main objective is to improve the performance and the intuitivity of the physical human-robot interaction. One obstacle to achieve this goal is the management of limitations such as joint position limitation, singularities and collisions with the environment. Indeed, in addition to performing a given principal task, human users must pay a close attention to the manipulator configuration in order to handle the kinematic limitations. The proposed anticipative algorithm aims at relieving the human user from having to deal with such limitations. The algorithm is first presented and detailed for each individual limitation of a planar RR serial robot. The framework developed to manage several limitations occurring simultaneously is then presented. Finally, experiments are performed in order to assess the performance of the algorithm.

Conventionally, a cable driven parallel mechanism (CDPM) pose is obtained through the forward kinematics from measurements of the cable lengths. However, this estimation method can be limiting for some applications requiring more precision. This paper proposes to use cable angle position sensors in addition to cable length measurements in order to improve the accuracy of such mechanisms. The robot pose is first obtained individually by the cable length measurements and the cable angle position measurements. A data fusion scheme combining these two types of measurements is then proposed in order to improve the CPDM accuracy. Finally, simulations and experiments are presented in order to assess the benefits of using cable angle position sensors on the CDPM.

Robots are currently used in some applications to enhance human performance and it is expected that human/robot interactions will become more frequent in the future. In order to achieve effective human augmentation, the cooperation must be very intuitive to the human operator. This paper presents a variable admittance control approach to improve system intuitivity. The proposed variable admittance law is based on the inference of human intentions using desired velocity and acceleration. Stability issues are discussed and a controller design example is given. Finally, experimental results obtained with a full-scale prototype of an intelligent assist device are presented in order to demonstrate the performance of the algorithm.