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.

Comfort and learnability assessment of a new soft robotic manipulator for minimally invasive surgery.

Laparoscopic surgeons perform precise and time consuming procedures while holding awkward poses in their upper body and arms. There is an ongoing effort to produce robotic tools for laparoscopic surgery that will simplify these tasks and reduce risk of errors to help both the surgeon and the patient. STIFF-FLOP is an ongoing EU FP7 project focusing on this by creating a stiffness controllable soft robotic manipulator. This paper reports on a study to test the soft manipulator's learnability and the effort associated with its use. The tests involved a limited prototype of the manipulator with a custom built test rig and EMG acquisition system. Task times and video recordings along with EMG waveforms from the forearm muscles of participants (n=25) were measured for objective assessment. A questionnaire was also provided to the participants for subjective assessment. The data shows that in average EMG levels were 25.9% less in RMS when using the STIFF-FLOP arm than when conventional laparoscopic tools were used. In terms of learnability, from the first to the second attempt on the STIFF-FLOP manipulator, elapsed time was reduced by an average of 32.1%. Further details and analysis of the EMG signals as well as time and questionnaire results is presented in the paper.

Endoscopic add-on stiffness probe for real-time soft surface characterisation in MIS.

This paper explores a novel stiffness sensor which is mounted on the tip of a laparoscopic camera. The proposed device is able to compute stiffness when interacting with soft surfaces. The sensor can be used in Minimally Invasive Surgery, for instance, to localise tumor tissue which commonly has a higher stiffness when compared to healthy tissue. The purely mechanical sensor structure utilizes the functionality of an endoscopic camera to the maximum by visually analyzing the behavior of trackers within the field of view. Two pairs of spheres (used as easily identifiable features in the camera images) are connected to two springs with known but different spring constants. Four individual indenters attached to the spheres are used to palpate the surface. During palpation, the spheres move linearly towards the objective lens (i.e. the distance between lens and spheres is changing) resulting in variations of their diameters in the camera images. Relating the measured diameters to the different spring constants, a developed mathematical model is able to determine the surface stiffness in real-time. Tests were performed using a surgical endoscope to palpate silicon phantoms presenting different stiffness. Results show that the accuracy of the sensing system developed increases with the softness of the examined tissue.

An indentation depth-force sensing wheeled probe for abnormality identification during minimally invasive surgery.

This paper presents a novel wheeled probe for the purpose of aiding a surgeon in soft tissue abnormality identification during minimally invasive surgery (MIS), compensating the loss of haptic feedback commonly associated with MIS. Initially, a prototype for validating the concept was developed. The wheeled probe consists of an indentation depth sensor employing an optic fibre sensing scheme and a force/torque sensor. The two sensors work in unison, allowing the wheeled probe to measure the tool-tissue interaction force and the rolling indentation depth concurrently. The indentation depth sensor was developed and initially tested on a homogenous silicone phantom representing a good model for a soft tissue organ; the results show that the sensor can accurately measure the indentation depths occurring while performing rolling indentation, and has good repeatability. To validate the ability of the wheeled probe to identify abnormalities located in the tissue, the device was tested on a silicone phantom containing embedded hard nodules. The experimental data demonstrate that recording the tissue reaction force as well as rolling indentation depth signals during rolling indentation, the wheeled probe can rapidly identify the distribution of tissue stiffness and cause the embedded hard nodules to be accurately located.

A new soft-tissue indentation model for estimating circular indenter 'force-displacement' characteristics.

Models that predict soft-tissue indentation forces have many important applications including estimation of interaction forces, palpation simulation, disease diagnosis, and robotic assistance. In many medical applications such as rehabilitation, clinical palpation, and manipulation of organs, characterizing soft-tissue properties mainly depends on the accurate estimation of indentation forces. A new indentation model for estimating circular indenter 'force-displacement' characteristics is presented in this paper. The proposed model is motivated by a 'force-displacement' soil-tool model and is computationally efficient. The main feature of the proposed model is that it can be used to predict the force variations for a variety of tools without the need for retuning the model parameters for each tool. A six-degree-of-freedom robot manipulator with force and position sensors is used to validate the indentation model. Measured force versus tool displacement data for lamb liver and kidney, for a variety of tool diameters, are presented and compared with the forces predicted by the model, showing good agreement (RMS error < 8 per cent).