Tytus Wojtara

Human-robot collaboration in precise positioning of a three-dimensional object

This paper deals with fundamental issues of human-robot cooperation in precise positioning of a flat object on a target. Based on the analysis of human-human interaction, two cooperation schemes are introduced. Several algorithms implementing these schemes are developed. A general theoretical framework for human/robot cooperation has been developed to represent these algorithms. The evaluation of the algorithms was carried out using our in-house made robot prototype and experiments by human subjects has demonstrated the effectiveness of our schemes. The main problem was the regulation of the robot-human interaction. Since the robot has no range sensors, it has to rely on the force and displacement information resulting from the interaction with human to understand human intention. The way the robot interprets these signal is crucial for smooth interaction. To be able to carry out a concrete task a simplification was made, in which robot and human do not directly hold the object but a frame to which the object and various sensors are attached.

Muscle synergy space: learning model to create an optimal muscle synergy.

Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however, remains a long-standing challenge. One effective method of choosing muscle combinations is to create a set containing the muscle combinations of only efficient behaviors, and then to choose combinations from that set. The notion of muscle synergy, which was introduced to divide muscle activations into a lower-dimensional synergy space and time-dependent variables, is a suitable tool relevant to the discussion of this issue. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to control behaviors. In this study, we investigated the mechanism the CNS may use to define the appropriate region and size of the synergy space when performing skilled behavior. Two indices were introduced in this study, one is the synergy stability index (SSI) that indicates the region of the synergy space, the other is the synergy coordination index (SCI) that indicates the size of the synergy space. The results on automatic posture response experiments show that SSI and SCI are positively correlated with the balance skill of the participants, and they are tunable by behavior training. These results suggest that the CNS has the ability to create optimal sets of efficient behaviors by optimizing the size of the synergy space at the appropriate region through interacting with the environment.

Muscle synergy stability and human balance maintenance.

BackgroundThe signals that the central nervous system (CNS) produces and sends to the muscles to effect movement are not entirely understood. Muscle synergy theory suggests that the central nervous system produces a small number of signals that pass through a network that distributes combinations of these signals to the muscles. Though these synergies are rather stable over time, some variability is present.MethodsHere, we investigated the variability of muscle synergy and defined a synergy stability index (SSI) to quantify it. We measured the activity of muscles responsible for maintaining lateral balance in humans standing on a platform that was subjected to lateral disturbance from the platform. We then calculated muscle synergies attributed to postural reflex and automatic response by using non-negative matrix factorization (NMF). Finally, from the calculated muscle synergies, we obtained SSI.ResultsWe observed a positive proportional relation between balance performance and SSI. Participants who were adept at maintaining balance were found to have invariant muscle synergies, and non-adept participants showed variable muscle synergies.ConclusionsThese results suggest that SSI can be used to quantitatively evaluate balance maintenance ability.

Artificial balancer - Supporting device for postural reflex

The evolutionarily novel ability to keep ones body upright while standing or walking, the human balance, deteriorates in old age or can be compromised after accidents or brain surgeries. With the aged society, age related balance problems are on the rise. Persons with balance problems are more likely to fall during their everyday life routines. Especially in elderly, falls can lead to bone fractures making the patient bedridden, weakening the body and making it more prone to other diseases. Health care expenses for a fall patient are often very high. There is a great deal of research being done on exoskeletons and power assists. However, these technologies concentrate mainly on the amplifications of human muscle power while balance has to be provided by the human themself. Our research has been focused on supporting human balance in harmony with the humans own posture control mechanisms such as postural reflexes. This paper proposes an artificial balancer that supports human balance through acceleration of a flywheel attached to the body. Appropriate correcting torques are generated through our device based on the measurements of body deflections. We have carried out experiments with test persons standing on a platform subject to lateral perturbations and ambulatory experiments while walking on a balance beam. These experiments have demonstrated the effectiveness of our device in supporting balance and the possibility of enhancing balance-keeping capability in human beings through the application of external torque.

Voluntary and Reflex Muscle Synergies in Upper Limbs

It is known that the human muscles can be controlled both intentionally and by automatic responses. However how exactly the neural signals received by the muscles are produced is still unknown. One of the concepts created to answer this question are the muscle synergies. This concept however, doesn’t take into account whether the neural signals are of voluntary or involuntary origin. Most researchers apply this concept to analyze exclusively automatic responses or exclusively voluntary movements or both without distinguishing between them. We propose an extended synergy model that explicitly accounts for both voluntary and involuntary neural signals and try to verify it experimentally.We examine reaching movements with and without constraints that provoke automatic responses. The general goal of this research is the creation of a measure of recovery level in upper limb rehabilitation after brain stroke, as well as, a rehabilitation assisting device. We introduce the synergy stability index and with experiments we show that the synergy stability is lower for movements with disturbance that provoke an involuntary movement.

Human-Robot Cooperation in Precise Positioning of a Flat Object

Abstract This paper deals with fundamental issues of human-robot cooperation in precise positioning of a flat object on a target. Based on the analysis of human-human interaction, two cooperation schemes are defined. An algorithm imitating one of these schemes is presented. A general mathematical framework for human/robot cooperation has been developed, based on which several algorithms are proposed. The evaluation of the algorithms was carried out using our in-house made robot prototype using a number of subjects has demonstrated the effectiveness of our ideas. The main problem was the regulation of the robot human interaction. Since the robot has no range sensors, it has to rely on the force and displacement information resulting from the interaction with human. The way the robot interprets these signal is crucial for smooth interaction. To be able to carry out a concrete task a simplification was made, in which robot and human do not directly hold the object but a frame to which the object and various sensors are attached. Based on our research results, we succeeded in installing a commercial platform.

The Rule of the Dependency Level of the Sensory Synergy in Recruiting Muscle Synergy

The concept of sensory and muscle synergies have reemerged in neuroscience as a possible mechanism adopted by the central nervous system (CNS) to deal with the complexity of the sensorimotor signaling in advanced mammals. Many studies have proposed various strategies to extract and deal with such as synergies: 1) to gain a deep insight into the neuromuscular system in human, and 2) to reconstruct robust neuro-base rehabilitation techniques for stroke patients. This study is a part of a series of studies that aim to build an automated neurorehabilitation tracking system based on understanding the link between the sensory synergy (SS) and the muscle synergy (MS). More precisely, here we are exploring the underlying mechanisms of how the CNS relies on SS feedback to recruit the proper MS when executing a certain movement. This study is derived from experimental analysis of automatic posture responses to the lateral ground perturbations of seven healthy subjects with various balance abilities. Results reveal that the dependency level among the calculated joint-acceleration-based SS are likely to be a key point for tuning the muscle synergy to ensure the quality of the resulting movement.