Research

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. With comfortable electronic-textiles, sensors can be embedded into clothing so that it is possible to record human movement outside the laboratory for long periods. However, a long-standing issue is how to deal with motion artefacts introduced by movement of clothing with respect to the body. Surprisingly, recent empirical findings suggest that cloth-attached sensor can actually achieve higher accuracy of activity recognition than rigid-attached sensor, particularly when predicting from short time-windows. In this work, a probabilistic model is introduced in which this improved accuracy and resposiveness is explained by the increased statistical distance between movements recorded via fabric sensing. The predictions of the model are verified in simulated and real human motion capture experiments, where it is evident that this counterintuitive effect is closely captured.

Robotic packaging of fresh leafy produce such as herbs and salads generally involves picking out a target mass from a pile or crate of plant material. Typically, for low-complexity parallel grippers,the weight picked can be controlled by varying the opening aperture. However, often individual strands of plant material get entangled with each other, causing more to be picked out than desired. This paper presents a simple spread-and-pick approach that significantly reduces the degree of entanglement in a herb pile when picking. Compared to the traditional approach of picking from an entanglement-free pointin the pile, the proposed approach results in a decrease of up to 29.06% of the variance in for separate homogeneous piles of fresh herbs. Moreover, it shows good generalisation with up to 55.53% decrease in picked weight variance for herbspreviously unseen by the system.

Enables the VIAs to regenerate dissipated energy from bidirectional rotation movement to charge a unidirectional electric storage. Up to 50% dissipated energy can be recovered theoretically, but also there flexibility in balancing task performance and energy cost.

Using Programming by Demonstration to teach robot learners generalisable skills relies on having effective human teachers. This paper aims to address two problems commonly observed in demonstration data sets that arise due to poor teaching strategies; undemonstrated states and ambiguous demonstrations. Overcoming these issues through the use of visual feedback and simple heuristic rules is investigated as a potential way of guiding novice users to more effectively teach robot learners to generalise a task. The proposed method intends to offer the user a more transparent understanding of the robot learner? model state during the teaching phase, to create a more interactive and robust teaching process. Results from a single-factor, three-phase repeated measures study with n = 30 participants, comparing the proposed feedback and heuristic rules set against an unguided condition, show a statistically significant (F(2, 58) = 7.952, p = 0.001) improvement of user teaching efficiency of approximately 180% when using the proposed feedback visualisation.

Sensors embedded into clothing for measuring human movement are becoming more widespread in research, with applications in clinical diagnostics or rehabilitation studies. A major issue with their use is the undesired effect of fabric motion artifacts corrupting movement signals. This paper presents a method for learning body movements, viewing the undesired motion as stochastic perturbations to the sensed motion, and utilising errors-in-variables models to eliminate these errors in the learning process. Experiments, both in simulation and with a physical fabric-mounted sensor, indicate improved prediction accuracy as compared to standard learning methods.

This paper presents a method for removing motion artefacts from fabric-embedded sensors. In this, an unsupervised latent space regression method is presented for learning body movements from fabric motion corrupted sensors, while simultaneously allowing for automatic recalibration.

Running is one of the most popular physical activities in the world, with multiple benefits including increasing life expectancy to preventing chronic diseases or conditions. On the other hand, the practise of running also gives rise to Running Related Injuries (RRI). Despite a number of studies having been conducted on RRIs, the literature is still lacking when it comes to risk factors, particularly the relationship between the running surface and RRIs is not clear.

The low-cost manufacturing and maintenance of prostheses is of vital importance to their successful deployment in developing countries. Low-cost prosthesis actuation is generally achieved by combining pre-programmed control strategies, with surface-electromyographic measurements taken from the residual limb. In a standard setting, these signals are measured with disposable gel electrodes. However, this limit on electrode reuse requires that prosthesis users have a stable supply of electrodes. Alternatively, the textile electrodes sewn from conductive thread are studied in the context of hand gesture recognition to consider their future use with low-cost prostheses. In this paper, it is demonstrated that textile electrodes can be applied for gesture recognition. To do so, surface electromyography (sEMG) experiments are run in South Africa on three amputees where they were asked to perform gestures with their phantom limb (i.e., the missing limb segment). A gesture recognition method is implemented, and the classification accuracy with data recorded from textile electrodes is compared to that from gel electrodes. Further analysis examining the relationship between classifier performance and physiological parameters are performed. Results show that textile electrodes can be used to perform accurate gesture recognition, and are comparable to disposable gel electrodes. This demonstrates that low-cost sensory systems are not barrier to myoelectric control in developing countries.