Functional Near-Infrared Spectroscopy (fNIRS) is a neuroimaging method which can be implemented with a wearable form factor. However, the data of fNIRS can be affected by motion artifact, which is conventionally processed offline using MATLAB-based software package via a bulky PC. This study trains a Support Vector Machine (SVM) algorithm and proposes a hardware design approach based on an FPGA to achieve the first real-time fNIRS motion artifact detection. The SVM hardware architecture proposed here utilizes a partially sequential–partially parallel implementation of the classification algorithm where Support Vector channels are consolidated into a single oversampled channel. A high classification accuracy of 97.42%, low FPGA resource utilization of 38,354 look-up tables and 6024 flip-flops with 10.92 us latency is achieved, outperforming conventional CPU SVM methods. These results show that an FPGA-based fNIRS motion artifact detector can be exploited whilst meeting real-time and resource constraints that are crucial in high-performance reconfigurable hardware systems.

Functional Near-Infrared Spectroscopy (fNIRS) and its extension, Diffuse Optical Tomography (DOT), are emerging non-invasive neuroimaging techniques that measure brain activities by monitoring changes in blood oxygenation using near infrared light. However, motion artifacts from subject movements in fNIRS/DOT data could severely undermine data quality. Current solutions typically rely on offline methods executed on conventional computers in laboratories/hospitals, limiting real-time applications and flexibility in wider environments. To address these limitations, we present an FPGA-based multi-channel real-time motion artifact detection system. The proposed system, tested against an expert-annotated dataset, showcases encouraging overall performance, with a minimal delay of 2.75 ms across 12-channel raw fNIRS data, and boasts a sensitivity rate of 85.28% and accuracy of 87.06%. This efficiency is achieved using less than 10% of FPGA resources, underscoring that the proposed real-time processing system holds the potential to be scaled up to 3630 channels. These results indicate a promising avenue towards real-time motion artifact processing in large-size multi-channel fNIRS/DOT data. Our design lays the groundwork for its application in areas including wearable real-time functional brain imaging, brain-computer interfaces, human-robot interaction, and surgical monitoring.

Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems. We achieved a high classification accuracy of 97.42%, low field-programmable gate array (FPGA) resource utilization of 38354 lookup tables and 6024 flip-flops, as well as low power consumption of 0.021 W in dynamic power. These results outperform conventional CPU support vector machine (SVM) methods and other state-of-the-art SVM implementations. This study has demonstrated that an FPGA-based fNIRS motion artifact classifier can be exploited while meeting low power and resource constraints, which are crucial in embedded hardware systems while keeping high classification accuracy.

This demonstration showcases a novel FPGA development pipeline for developing a low-power and real-time motion artefact detection module for a wearable functional near-infrared spectroscopy (fNIRS) processing system. We provide a brief overview of the development design flow for our learning-based motion artefact detector in a heterogeneous platform, as well as the evaluation method for removing motion artefacts, which are unwanted signal variations that occur due to subject motion during data acquisition.

This paper presents a single case study of an on-going study evaluating cortical association with facilitation and management of vibro-tactile stimulation applied prior to voluntary muscle contraction. The study consisted of three repetitions of relaxation phase during which vibrations are applied, and a contraction phase. EEG and EMG data was collected to determine muscle and brain activation patterns. The EEG analysis of the mu waves during relaxation + vibration phase seem to indicate sensory cortex activation during focal muscle vibrations. With repetitiveness of vibrations, an increase in maximal calculated mu power was observed that could suggest optimization of the muscle fibers prior to the contraction. When contraction is performed, mu waves are desynchronizing with the movement execution. The analysis of the last relaxation period indicate that the muscle itself facilitates the last contraction locally possibly due to cortical learning.

This paper introduces a new haptic-supported software framework that facilitates the set up of different types of group interaction. The framework consists of multiple open source libraries supporting a range of external devices and services (e.g. Microsoft Kinect, cameras, Arduino controllers, sensors, AR tracking, remote haptic interaction,). To date three pilot studies have been conducted to test out the framework based on some existing benchmarks from the literature. Benchmarking studies have shown the flexibility and stability of the framework to devise interactive tasks in different social environments. It pointed out that this framework has the potential to be applied for socially assistive robotics field.

This paper discusses the design of a Redundant Drive Joint with Double Actuation (RDJ-DA) to produce controlled compliant motions over a higher bandwidth. First, our strategies on mechanical and controller designs to produce compliant motions are described, and the basic structure and impedance control of the RDJ-DA with internal serial structure explained. The standard form of the basic structure is introduced using a set of parameters which can be used to express the inertia property of RDJ-DA. Second, a problem statement for the design of RDJ-DAs is described after pointing out that required torques of the second actuator of the RDJ-DA could be large. Then, a basic idea of introducing springs into the second actuator in parallel is proposed as a part of the structural design of RDJ-DA to reduce the large torques. Simulations are conducted to find out a set of the design parameters of RDJ-DA which obtains a higher natural frequency of the output joint admittance satisfying both the desired output joint admittance and the limitations of two identical motors on their performance. Finally, a prototype design of RDJ-DA using the obtained set of the design parameters is presented.

This paper proposes impedance control of redundant drive joints with double actuation (RDJ-DA) to produce compliant motions with the future goal of higher bandwidth. First, to reduce joint inertia, a double-input-single-output mechanism with one internal degree of freedom (DOF) is presented as part of the basic structure of the RDJ-DA. Next, the basic structure of RDJ-DA is further explained and its dynamics and statics are derived. Then, the impedance control scheme of RDJ-DA to produce compliant motions is proposed and the validity of the proposed controller is investigated using numerical examples.

Smooth trajectories are essential for safe interaction in between human and a haptic interface. Different methods and strategies have been introduced to create such smooth trajectories. This paper studies the creation of human-like movements in haptic interfaces, based on the study of human arm motion. These motions are intended to retrain the upper limb movements of patients that lose manipulation functions following stroke. We present a model that uses higher degree polynomials to define a trajectory and control the robot arm to achieve minimum jerk movements. It also studies different methods that can be driven from polynomials to create more realistic human-like movements for therapeutic purposes.