This paper presents a 0.13-μm CMOS system-on-chip (SoC) designed for multilead ECG recording in low-power wearable applications. The fully integrated system features eight fully differential recording channels, capable of extracting 11 ECG leads, a fully differential 10-bit capacitive successive approximation register (SAR) analog-to-digital converter (ADC), a digital controller with a custom master SPI interface, low-dropout regulators (LDOs), and level shifters to facilitate integration. The ECG recording channels are implemented using a novel fully differential current mirror-based bioamplifier circuit, which offers tunable gain and intrinsic band-pass filtering. The bioamplifier achieves an input-referred noise of 1.8 μVrmsover a 0.3-500 Hz bandwidth, a total harmonic distortion (THD) of 0.234%, and consumes 4.24 μA. The ADC achieves an effective number of bits (ENOB) of 9.18 while consuming 11 μA. Overall, the SoC draws 347 μW from a 1.2-V supply and demonstrates successful 11-lead ECG recordings and PQRST wave identification.

This work presents a reinforcement learning-based switching control mechanism to autonomously move a ferromagnetic object (representing a milliscale robot) around obstacles within a constrained environment in the presence of disturbances. This mechanism can be used to navigate objects (e.g., capsule endoscopy, swarms of drug particles) through complex environments when active control is a necessity but where direct manipulation can be hazardous. The proposed control scheme consists of a switching control architecture implemented by two sub-controllers. The first sub-controller is designed to employ the robot’s inverse kinematic solutions to do an environment search for the to-be-carried ferromagnetic particle while being robust to disturbances. The second sub-controller uses a customized rainbow algorithm to control a robotic arm, i.e., the UR5 robot, to carry a ferromagnetic particle to a desired position through a constrained environment. For the customized Rainbow algorithm, Quantile Huber loss from the Implicit Quantile Networks (IQN) algorithm and ResNet are employed. The proposed controller is first trained and tested in a real-time physics simulation engine (PyBullet). Afterward, the trained controller is transferred to a UR5 robot to remotely transport a ferromagnetic particle in a real-world scenario to demonstrate the applicability of the proposed approach. The experimental results on the UR5 robot show an average success rate of 98.86% over 30 episodes for randomly generated trajectories, demonstrating the viability of the proposed approach for real-life applications. In addition, two classical path finding approaches, Attractor Dynamics and the execution extended Rapidly-Exploring Random Trees (ERRT), are also investigated and compared to the RL-based method. The proposed RL-based algorithm is shown to achieve performance comparable to that of the tested classical path planners whilst being more robust to deploy in dynamical environments. Note to Practitioners —Deep reinforcement learning methods have been widely applied in computer games and simulations. However, employing these algorithms for practical, real-world applications such as robotics becomes challenging due to the difficulty of obtaining training samples. This paper predominantly focuses on bridging the gap between simulations and the real-world implementation of a reinforcement learning algorithm for a robotic application in the context of miniaturized drug delivery robots and robotic capsule endoscopes. This paper presents the derivation and experimental validation of a reinforcement learning-based algorithm for controlling a magnetically-actuated small-scale robot within a simplified model of the large intestine in the presence of disturbances. We demonstrate the possibility of training a high-fidelity reinforcement learning algorithm fully within a simulated environment before deploying it as-is in a real-world scenario by carrying out different experiments and simulations. Implementing the presented control framework complements a large body of this work, and the results offer a feasibility study of using reinforcement learning algorithms in practice.

Internet-delivered psychological treatments (IDPT) are seen as an effective and scalable pathway to improving the accessibility of mental healthcare. Within this context, treatment adherence is an especially pertinent challenge to address due to the reduced interaction between healthcare professionals and patients. In parallel, the increase in regulations surrounding the use of personal data, such as the General Data Protection Regulation (GDPR), makes data minimization a core consideration for real-world implementation of IDPTs. Consequently, this work proposes a Self-Attention-based deep learning approach to perform automatic adherence forecasting, while only relying on minimally sensitive login/logout-timestamp data. This approach was tested on a dataset containing 342 patients undergoing Guided Internet-delivered Cognitive Behavioral Therapy (G-ICBT) treatment. Of these 342 patients, 101 ( ∼ 30%) were considered non-adherent (dropout) based on the adherence definition used in this work (i.e. at least eight connections to the platform lasting more than a minute over 56 days). The proposed model achieved over 70% average balanced accuracy, after only 20 out of the 56 days ( ∼ 1/3) of the treatment had elapsed. This study demonstrates that automatic adherence forecasting for G-ICBT, is achievable using only minimally sensitive data, thus facilitating the implementation of such tools within real-world IDPT platforms.

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

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 a novel particle swarm optimization (PSO) algorithm that combines the strengths of several PSO variants into a single competitive algorithm. This novel algorithm, named Time Adaptive Dual Particle Swarm Optimization (TAD-PSO), is comprised of two specialized populations, with one focusing on exploration of the search space and the other on exploitation. The main population, specialized in exploration, uses orthogonal learning to create information-rich exemplars which intelligently guide particle movement throughout the search space. The auxiliary population uses a PSO variant known for its very fast convergence speed, and thus very high performance on unimodal problems. This population is specialized in exploitation of the interesting local minima. The main population size decays linearly, to foster exploration early and convergence in the later stages of the optimization procedure. Additionally, TAD-PSO does not have the topological structure of the swarm as an algorithm hyper-parameter, making it a fast and simple algorithm to apply to new problems. TAD-PSO was tested extensively and compared to 6 widely used PSO variants on 19 benchmark problems, for 10, 30 and 100 dimensions. TAD-PSO consistently ranked first in each dimensional space, making it a competitive optimization algorithm on both unimodal and multimodal problems.

In recent years, gesture-based interfaces have been explored in order to control robots in non-traditional ways. These require the use of systems that are able to track human body movements in 3D space. Deploying Mo-cap or camera systems to perform this tracking tend to be costly, intrusive, or require a clear line of sight, making them ill-adapted for artistic performances. In this paper, we explore the use of consumer-grade armbands (Myo armband) which capture orientation information (via an inertial measurement unit) and muscle activity (via electromyography) to ultimately guide a robotic device during live performances. To compensate for the drop in information quality, our approach rely heavily on machine learning and leverage the multimodality of the sensors. In order to speed-up classification, dimensionality reduction was performed automatically via a method based on Random Forests (RF). Online classification results achieved 88% accuracy over nine movements created by a dancer during a live performance, demonstrating the viability of our approach. The nine movements are then grouped into three semantically-meaningful moods by the dancer for the purpose of an artistic performance achieving 94% accuracy in real-time. We believe that our technique opens the door to aesthetically-pleasing sequences of body motions as gestural interface, instead of traditional static arm poses.

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.

Recently, robotics has been seen as a key solution to improve the quality of life of amputees. In order to create smarter robotic prosthetic devices to be used in an everyday context, one must be able to interface them seamlessly with the end-user in an inexpensive, yet reliable way. In this paper, we are looking at guiding a robotic device by detecting gestures through measurement of the electrical activity of muscles captured by surface electromyography (sEMG). Reliable sEMG-based gesture classifiers for end-users are challenging to design, as they must be extremely robust to signal drift, muscle fatigue and small electrode displacement without the need for constant recalibration. In spite of extensive research, sophisticated sEMG classifiers for prostheses guidance are not yet widely used, as systems often fail to solve these issues simultaneously. We propose to address these problems by employing Convolutional Neural Networks. Specifically as a first step, we demonstrate their viability to the problem of gesture recognition for a low-cost, low-sampling rate (200Hz) consumer-grade, 8-channel, dry electrodes sEMG device called Myo armband (Thalmic Labs) on able-bodied subjects. To this effect, we assessed the robustness of this machine learning oriented approach by classifying a combination of 7 hand/wrist gestures with an accuracy of ∼97.9% in real-time, over a period of 6 consecutive days with no recalibration. In addition, we used the classifier (in conjunction with orientation data) to guide a 6DoF robotic arm, using the armband with the same speed and precision as with a joystick. We also show that the classifier is able to generalize to different users by testing it on 18 participants.