Current topics:

Researcher: Satoshi Endo, Neha Das

Motivation

Our research efforts centre on a continuous, non-invasive monitoring system for patients with movement deficits, and patient-specific adaptive rehabilitation/healthcare technologies. Neurological disorders such as Parkinson’s disease (PD) has complex phenomenology not only across patients but also within themselves across time. Thus, continuous and non-invasive monitoring of clinical states of the patients during daily activities could provide valuable support for healthcare management such as a medication plan or physiotherapy training. Nevertheless, the current healthcare models typically involve clinical evaluations performed very sparsely (e.g. every 4 to 6 months) because of the physicians’ time and economic constraints. Thus, we are developing robust control-oriented modelling and estimation methods for closely monitoring changes in the patient's state and neurological deficits. These include employing various forms of treatment together with a low-cost wearable sensor (e.g., a smart watch).

Research question 

• Quantitative characterisation of clinical symptoms and interaction effects of various factors such as medication and fitness level in unconstrained (i.e. daily) environments.
• Robust modelling and predicting of clinical state of patients against noisy measurements of movements in low-cost sensors.
• Transferring the clinical insights into control applications for optimising treatment efficacy.

Approach

We are currently developing a machine learning technique for reliably estimating progression and fluctuation of motor deficits in patients with PD using inertia measurements available from a low-cost wearable sensor (e.g., a smart watch). In particular, we employ Gaussian Processes (GP) in order to ensure that the errors from low-cost devices are transparently and robustly processed by the model for estimating a current PD state. 

Key results and achievements 

• Extraction of tremor information from inertia measurements during daily activities using continuous wavelet transform and machine learning.
• Parameterisation of PD-specific motions and application of GP for severity estimation

Researcher: Satoshi Endo

Motivation

Our research interest centres around neuroscientific understandings of human movement control in dynamic environments, with a special emphasis on motor coordination with another agent including a robotic system. We conduct scientific research about how neuroscientific variables such as perception, cognition, and motor control influence the interaction dynamics of multi-agent coordination. The results are used to derive techniques for effectively controlling mult-agent interactions in, for example, assistive technologies.  

Research questions

• Perceptual and cognitive factors attributing observation of external input as intentional or erroneous.
• Evolution of cooperative strategies between agents
• Models of intention recognition based on interaction dynamics and perceptual cues

Approach

• Our method consists of modelling human behavior as a stochastic dynamical system and studying how the coordination is moderated by human agents. Human-driven models are then implemented for adaptive control of a robotic system in HRI tasks.

Key results and achievements

• Modelling multi-agent coordination as a dynamical system to statistically characterise the coordination strategy.
• Application of bio-inspired motions and human models for object manipulation in pHRI. 

Researcher: Samuel Tesfazgi, Satoshi Endo

Motivation

Whenever humans execute a desired motor task, the produced joint torques consist of a feedforward and a feedback component. According to a theory of sensorimotor control, the feedforward component is determined a priori using internal models. During execution, deviations from the planned behavior are compensated by the feedback component, which consists of effects of muscle intrinsic viscoelastic properties, short- and long-latency reflexes, and voluntary feedback at cognitive level. As voluntary feedback possesses the longest delays, it may not be sufficient for the maintenance of stability. Thus, the central nervous system must rely on the intrinsic and reflexive feedback components, which can be modeled by a mechanical impedance consisting of inertia, damping, and stiffness. While the inertia is solely determined by limb kinematics, the latter two can be influenced by muscle activation, which enables the a priori cognitive modulation of the respective characteristics. In addition to fundamental insights into motor control, the analysis of such modulation strategies may yield models that are transferable to robotics domains such as human robot collaboration and variable impedance control.

Research questions

• Estimation of impedance characteristics in multi-joint arm movements
• Analysis and data-driven modeling of impedance modulation strategies
• Transfer of models to robotics domains, e.g., human robot interaction

Approach

We have designed an experimental framework specifically tailored to meet the requirements of impedance estimation during multi-joint arm movements. The experimental apparatus is capable of inducing short, precise disturbances during otherwise free motion of the participants. As we intend to analyze transient impedance modulation strategies, we have developed a single trial feedback component isolation method. The obtained impedance characteristics are included in stochastic dynamical systems that model human motor behavior.

Key results and achievements

• Design of a framework for multi-joint arm impedance estimation
• Development of a single trial feedback component isolation method
   

Researcher: Satoshi Endo

Motivation 

Various forms of assistive control have been introduced to help users in operating machines as found in smart vehicles and prosthetics for example. Commonly, semi-autonomous controllers are designed to maximise the task performance to compensate for suboptimal inputs by humans, for example, due to a lack of expertise or task knowledge. However, from a user perspective, introducing semi-autonomous control directly reduces controllability of the system, and a controller which maximises the performance may not necessarily bring positive user experience. This can lead to an undesirable outcome in long-term interaction. Therefore, we research and evaluate control designs from user perspectives by studying how well the autonomous system is embodied in the "control system" of the user by adopting psychological tools and theories.

Research questions

• Evaluating the quality of human-in-the-loop control using the psychological construct of embodiment in a haptic human-machine interaction tasks
• Human behaviour modelling and characterisation
• Assessment of haptic feedbacks

Approach

We have designed a haptic human-machine interaction task to evaluate the quality of human-in-the-loop control using a psychological construct of embodiment. For this task, we composed an experimental framework consisting of a haptic assistance and a virtual environment broadcasted through virtual reality goggles. With this framework, we tested the perceived embodiment under different assistive control algorithms in a reaching task with human participants.

Key results and achievements

• Design of an experimental setup consisting of a 2-DoF haptic device and a virtual environment for human studies.
• Investigation of subjective embodiment using psychometric scales.