Ali Shafti

Embedded electro-conductive yarn for shape sensing of soft robotic manipulators.

Flexible soft and stiffness-controllable surgical manipulators enhance the manoeuvrability of surgical tools during Minimally Invasive Surgery (MIS), as opposed to conventional rigid laparoscopic instruments. These flexible and soft robotic systems allow bending around organs, navigating through complex anatomical pathways inside the human body and interacting inherently safe with its soft environment. Shape sensing in such systems is a challenge and one essential requirement for precise position feedback control of soft robots. This paper builds on our previous work integrating multiple optical fibres into a soft manipulator to estimate the robots pose using light intensity modulation. Here, we present an enhanced version of our embedded bending/shape sensor based on electro-conductive yarn. The new system is miniaturised and able to measure bending behaviour as well as elongation. The integrated yarn material is helically wrapped around an elastic strap and protected inside a 1.5mm outer-diameter stretchable pipe. Three of these resulting stretch sensors are integrated in the periphery of a pneumatically actuated soft manipulator for direct measurement of the actuation chamber lengths. The capability of the sensing system in measuring the bending curvature and elongation of the arm is evaluated.

Wearable embroidered muscle activity sensing device for the human upper leg

Within the last decade, running has become one of the most popular physical activities in the world. Although the benefits of running are numerous, there is a risk of Running Related Injuries (RRIs) of the lower extremities. Electromyography (EMG) techniques have previously been used to study causes of RRIs, but the complexity of this technology limits its use to a laboratory setting. As running is primarily an outdoors activity, this lack of technology acts as a barrier to the study of RRIs in natural environments. This study presents a minimally invasive wearable muscle sensing device consisting of jogging leggings with embroidered surface EMG (sEMG) electrodes capable of recording muscle activity data of the quadriceps group. To test the use of the device, a proof of concept study consisting of N = 2 runners performing a set of 5 km running trials is presented in which the effect of running surfaces on muscle fatigue, a potential cause of RRIs, is evaluated. Results show that muscle fatigue can be analysed from the sEMG data obtained through the wearable device, and that running on soft surfaces (such as sand) may increase the likelihood of suffering from RRIs.Within the last decade, running has become one of the most popular physical activities in the world. Although the benefits of running are numerous, there is a risk of Running Related Injuries (RRIs) of the lower extremities. Electromyography (EMG) techniques have previously been used to study causes of RRIs, but the complexity of this technology limits its use to a laboratory setting. As running is primarily an outdoors activity, this lack of technology acts as a barrier to the study of RRIs in natural environments. This study presents a minimally invasive wearable muscle sensing device consisting of jogging leggings with embroidered surface EMG (sEMG) electrodes capable of recording muscle activity data of the quadriceps group. To test the use of the device, a proof of concept study consisting of N = 2 runners performing a set of 5 km running trials is presented in which the effect of running surfaces on muscle fatigue, a potential cause of RRIs, is evaluated. Results show that muscle fatigue can be analysed from the sEMG data obtained through the wearable device, and that running on soft surfaces (such as sand) may increase the likelihood of suffering from RRIs.

FourByThree: Imagine humans and robots working hand in hand

Since December 2014, FourByThree Project (“Highly customizable robotic solutions for effective and safe human robot collaboration in manufacturing applications”) is developing a new generation of modular industrial robotic solutions that are suitable for efficient task execution in collaboration with humans in a safe way and are easy to use and program by factory workers. This paper summarizes the key technologies that are used to achieve this goal.

Designing embroidered electrodes for wearable surface electromyography

Muscle activity monitoring or Electromyography (EMG) is useful in gait analysis, injury prevention, computer or robot interfaces and assisting patients with communication difficulties. However, EMG is typically invasive or obtrusive, expensive and difficult to use for untrained users. A possible solution is textile-based surface EMG (sEMG) integrated into clothing and used as a wearable device. This is, however, challenging due to (i) uncertainties in the electrical properties of conductive threads used to construct electrodes, (ii) imprecise fabrication technologies (e.g., embroidery, sewing), and (iii) a lack of standardisation in the choice of design variables. This paper, for the first time, provides a design guide for such sensors by performing a thorough examination of the effect of design variables on sEMG quality. Electrical characterisation and sEMG measurements are performed, considering the effects of manufacturing imprecision. Results show that the imprecisions in digital embroidery lead to a trade-off between low electrode resistance and high consistency. An optimum set of variables for this trade-off is identified and tested with sEMG during a variable force isometric grip exercise with n=6 participants and compared with traditional gel-based electrodes. Results show that thread-based electrodes provide a similar level of sensitivity to force variation as gel-based electrodes with about 90% correlation with expected linear behaviour.

Embroidered Electromyography: A Systematic Design Guide

Muscle activity monitoring or electromyography (EMG) is a useful tool. However, EMG is typically invasive, expensive and difficult to use for untrained users. A possible solution is textile-based surface EMG (sEMG) integrated into clothing as a wearable device. This is, however, challenging due to 1) uncertainties in the electrical properties of conductive threads used for electrodes, 2) imprecise fabrication technologies (e.g., embroidery, sewing), and 3) lack of standardization in design variable selection. This paper, for the first time, provides a design guide for such sensors by performing a thorough examination of the effect of design variables on sEMG signal quality. Results show that imprecisions in digital embroidery lead to a trade-off between low electrode impedance and high manufacturing consistency. An optimum set of variables for this trade-off is identified and tested with sEMG during a variable force isometric grip exercise with n = 12 participants, compared with conventional gel-based electrodes. Results show that thread-based electrodes provide a similar level of sensitivity to force variation as gel-based electrodes with about 90% correlation to expected linear behavior. As proof of concept, jogging leggings with integrated embroidered sEMG are made and successfully tested for detection of muscle fatigue while running on different surfaces.

Towards safer obstacle avoidance for continuum-style manipulator in dynamic environments

The flexibility and dexterity of continuum manipulators in comparison with rigid-link counterparts have become main features behind their recent popularity. Despite of that, the problem of navigation and motion planning for continuum manipulators turns out to be demanding tasks due to the complexity of their flexible structure modelling which in turns complicates the pose estimation. In this paper, we present a real-time obstacle avoidance algorithm for tendon-driven continuum-style manipulator in dynamic environments. The algorithm is equipped with a non-linear observer based on an Extended Kalman Filter to estimate the pose of every point along the manipulators body. The overall algorithm works well for a model of a single-segment continuum manipulator in a real-time simulation environment with moving obstacles in the workspace of manipulators.

Real-time pose estimation and obstacle avoidance for multi-segment continuum manipulator in dynamic environments

In this paper, we present a novel pose estimation and obstacle avoidance approach for tendon-driven multi-segment continuum manipulators moving in dynamic environments. A novel multi-stage implementation of an Extended Kalman Filter is used to estimate the pose of every point along the manipulators body using only the position information of each segment tip. Combined with a potential field, the overall algorithm will guide the manipulator tip to a desired target location and, at the same time, keep the manipulator body safe from collisions with obstacles. The results show that the approach works well in a real-time simulation environment that contains moving obstacles in the vicinity of the manipulator.