Intention-based Control Research Articles

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals.

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

Magnetic-based detection of muscular contraction for controlling hand prosthesis

Muscle contraction information from the residual upper-limb of an amputee can generate intention-based control to prosthetic devices. There are several approaches such as electromyography (EMG), force myography (FMG), and mechanomyography (MMG) that can directly or indirectly measure muscular contractions. However, these techniques suffer from the problems like ambient noise, motion artifacts, inappropriate contact with the skin, inaccurate output, and high cost of measurement. In this work, a novel magnetic-based Hall myography (HMG) technique is introduced that can precisely measure the muscular contractions from the subject's upper-limb. The sensor setup consists of a magnetic coupler and Hall element assembly separated by a mechanical spring. Any contraction from the muscle produces gradual movement of the magnet towards the hall element. This change in displacement is sensed by the hall element and is then translated to a 0–5 V linear output using appropriate preprocessing hardware and software. The sensor performance was evaluated in terms of sensitivity, repeatability, hysteresis, and frequency response. In detecting muscle contractions from the forearm of subjects, the sensor showed highly correlated output (r > 0.94, p-value<0.0001) with the EMG sensor. Moreover, the sensor showed better signal stability and similar dynamic behavior as compared to the EMG sensor. Further, the utility of the sensor was tested for controlling hand prosthesis. Subjects wearing the sensor were able to control the flexion of prosthetic hand fingers proportionally according to their intensity of muscle contraction. With several advantages, Hall myography can replace the EMG technique for prosthesis control application.

Robotic-Assisted Rehabilitation Trainer Improves Balance Function in Stroke Survivors

Nerve injury after stroke leads to disorders of locomotion and a declining balance function, which increases the risk of falling. Restriction of pelvic motions can hinder successful rehabilitation, hence a robotic-assisted rehabilitation trainer (RART) is proposed to assist patients in controlling the pelvic motions via force field. The mechanical design, kinetic framework, and the intention-based controller of the RART are introduced in this paper. Based on an intention-based control strategy, the robot generates force field that affects the pelvic motions (vertical and lateral motion, rotation, and obliquity) via a compliant pelvic brace while the patient is walking on ground. A control experiment with 16 hemiplegic patients is carried out to examine the effects on recovery of the balance function. Clinical evaluations and gait analysis are performed before and after the treatment. The experimental results show that the proposed control method is effective in motion recognition, and significant improvements of the balance function, gait speed, stride, stride frequency, Fugl-Meyer assessment, peak knee, and hip flexion angle during swing phase for their affected side are observed in the comparisons within the RART group. These preliminary results demonstrate that the proposed robot with force field and visual feedback may be effective in improving the balance function.

Development of a sEMG-based torque estimation control strategy for a soft elbow exoskeleton

Motor dysfunction has become a serious threat to the health of older people and the patients with neuromuscular impairment. The application of exoskeleton to motion assistance has received increasing attention due to its promising prospects. The major contribution of this paper is to develop a joint torque estimation control strategy for a soft elbow exoskeleton to provide effective power assistance. The surface electromyography signal (sEMG) from biceps is utilized to estimate the motion intension of wearer and map into the real-time elbow joint torque. Moreover, the control strategy fusing the estimated joint torque, estimated joint angle from inertial measurement unit and encoder feedback signal is proposed to improve motion assistance performance. Finally, further experimental investigations are carried out to compare the control effectiveness of the proposed intention-based control strategy to that of the proportional control strategy. The experimental results indicate that the proposed control strategy provides better performance in elbow assistance with different loads, and the average efficiency of assistance with heavy load is about 42.66%.

FAITH, BELIEF, AND CONTROL

Abstract In this paper, I solve a puzzle generated by three conflicting claims about the relationship between faith, belief, and control: according to (1) the Identity Thesis, faith is a type of belief, and according to (2) Fideistic Voluntarism, we sometimes have control over whether or not we have faith, but according to (3) Doxastic Involuntarism, we never have control over what we believe. To solve the puzzle, I argue that the Identity Thesis is true, but that either (2) or (3) is false, depending on how we understand "control." I distinguish two notions of control: direct intention-based control and indirect reflective control. I argue that though we have direct intention-based control over neither belief nor faith, we have indirect reflective control over each of them. Moreover, indirect reflective control helps explain how we can be held accountable for each.

In defense of doxastic blame

In this paper I articulate a view of doxastic control that helps defend the legitimacy of our practice of blaming people for their beliefs. I distinguish between three types of doxastic control: intention-based, reason-based, and influence-based. First I argue that, although we lack direct intention-based control over our beliefs, such control is not necessary for legitimate doxastic blame. Second, I suggest that we distinguish two types of reason-responsiveness: sensitivity to reasons and appreciation of reasons. I argue that while both capacities are necessary for satisfying the control condition, neither is sufficient. Finally, I defend an influence-based view of doxastic control according to which we have the capacity to execute intentions to engage in reflection that causally influences our beliefs in positive epistemic ways. This capacity is both necessary and sufficient for satisfying the control condition for legitimate doxastic blame. I end by defending the view from two objections: that reflection is not necessary for meeting the control condition, and that it is not sufficient.

Sensory-based mechanism for delayed motor intention

Prospective motor learning (PML) can be defined as learning an action to be performed in the future. The privileged retrieval mechanism behind this delayed motor performance remains unknown. From a motor control and learning perspective, we may conceive of two forms of retrieval: a stimulus- and an intention-based control. Retrieval from intention-based control is based on the anticipation of intended sensory effects related to an action in order to select and control the appropriate motor procedure (i.e., the ideomotor mechanism). In contrast, in a stimulus-based control a connection between stimuli-features and their related action-features is stored in the memory and serves as the retrieval mechanism. In this view, action retrieval from external stimuli is based on the detection of events in the environment to perform the intended behaviour (i.e., the sensorimotor mechanism). In this study, we report an advantage in the action retrieval for participants who use an intention-based mode of control in comparison to a stimulus-based control. Furthermore, a control task reveals that the intention-based advantage is specific to PML. Our findings show that PML is benefited by mental anticipation of a sensory effect that is efficiently processed through an ideomotor mechanism to fulfil delayed motor intentions.

Controlling a robot with intention derived from motion.

We present a novel, sophisticated intention-based control system for a mobile robot built from an extremely inexpensive webcam and radio-controlled toy vehicle. The system visually observes humans participating in various playground games and infers their goals and intentions through analyzing their spatiotemporal activity in relation to itself and each other, and then builds a coherent narrative out of the succession of these intentional states. Starting from zero information about the room, the rules of the games, or even which vehicle it controls, it learns rich relationships between players, their goals and intentions, probing uncertain situations with its own behavior. The robot is able to watch people playing various playground games, learn the roles and rules that apply to specific games, and participate in the play. The narratives it constructs capture essential information about the observed social roles and types of activity. After watching play for a short while, the system is able to participate appropriately in the games. We demonstrate how the system acts appropriately in scenarios such as chasing, follow-the-leader, and variants of tag.