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Tactile information acquired through palpation plays a crucial role in relation to surface characterisation and tissue differentiation - an essential clinical requirement during surgery. In the case of Minimally Invasive Surgery, access is restricted, and tactile feedback available to surgeons is therefore reduced. This paper presents a novel stiffness controllable, dynamic force range sensor that can provide remote haptic feedback. The sensor has an abraded optical fibre integrated into a silicone dome. Forces applied to the dome change the curvature of the optical fibres, resulting in light attenuation. By changing the pressure within the dome and thereby adjusting the sensor's stiffness, we are able to modify the force measurement range. Results from our experimental study demonstrate that increasing the pressure inside the dome increases the force range whilst decreasing force sensitivity. We show that the maximum force measured by our sensor prototype at 20 mm/min was 5.02 N, 6.70 N and 8.83 N for the applied pressures of 0 psi (0 kPa), 0.5 psi (3.45 kPa) and 1 psi (6.9 kPa), respectively. The sensor has also been tested to estimate the stiffness of 13 phantoms of different elastic moduli. Results show the elastic modulus sensing range of the proposed sensor to be from 8.58 to 165.32 kPa. [ABSTRACT FROM AUTHOR]

Tactile information acquired through palpation plays a crucial role in relation to surface characterisation and tissue differentiation - an essential clinical requirement during surgery. In the case of Minimally Invasive Surgery, access is restricted, and tactile feedback available to surgeons is therefore reduced. This paper presents a novel stiffness controllable, dynamic force range sensor that can provide remote haptic feedback. The sensor has an abraded optical fibre integrated into a silicone dome. Forces applied to the dome change the curvature of the optical fibres, resulting in light attenuation. By changing the pressure within the dome and thereby adjusting the sensor’s stiffness, we are able to modify the force measurement range. Results from our experimental study demonstrate that increasing the pressure inside the dome increases the force range whilst decreasing force sensitivity. We show that the maximum force measured by our sensor prototype at 20 mm/min was 5.02 N, 6.70 N and 8.83 N for the applied pressures of 0 psi (0 kPa), 0.5 psi (3.45 kPa) and 1 psi (6.9 kPa), respectively. The sensor has also been tested to estimate the stiffness of 13 phantoms of different elastic moduli. Results show the elastic modulus sensing range of the proposed sensor to be from 8.58 to 165.32 kPa.

Introduction: It is crucial to identify neurodevelopmental disorders in infants early on for timely intervention to improve their long-term outcomes. Combining natural play with quantitative measurements of developmental milestones can be an effective way to swiftly and efficiently detect infants who are at risk of neurodevelopmental delays. Clinical studies have established differences in toy interaction behaviors between full-term infants and pre-term infants who are at risk for cerebral palsy and other developmental disorders. Methods: The proposed toy aims to improve the quantitative assessment of infant-toy interactions and fully automate the process of detecting those infants at risk of developing motor delays. This paper describes the design and development of a toy that uniquely utilizes a collection of soft lossy force sensors which are developed using optical fibers to gather play interaction data from infants laying supine in a gym. An example interaction database was created by having 15 adults complete a total of 2480 interactions with the toy consisting of 620 touches, 620 punches--"kick substitute," 620 weak grasps and 620 strong grasps. Results: The data is analyzed for patterns of interaction with the toy face using a machine learning model developed to classify the four interactions present in the database. Results indicate that the configuration of 6 soft force sensors on the face created unique activation patterns. Discussion: The machine learning algorithm was able to identify the distinct action types from the data, suggesting the potential usability of the toy. Next steps involve sensorizing the entire toy and testing with infants. [ABSTRACT FROM AUTHOR]

Soft robotic grippers are required for power grasping of objects without inducing damage. Additive manufacturing can be used to produce custom-made grippers for industrial robots, in which soft joints and links are additively manufactured. In this study, a monoblock soft robotic gripper having three geometrically gradient fingers with soft sensors was designed and additively manufactured for the power grasping of spherical objects. The monoblock structure design reduces the number of components to be assembled for the soft gripper, and the gripper is designed with a single cavity to enable bending by the application of pneumatic pressure, which is required for the desired actuation. Finite element analysis (FEA) using a hyperelastic material model was performed to simulate the actuation. A material extrusion process using a thermoplastic polyurethane (TPU) was used to manufacture the designed gripper. Soft sensors were produced by a screen printing process that uses a flexible material and ionic liquids. The grasping capability of the manufactured gripper was experimentally evaluated by changing the pneumatic pressure (0–0.7 MPa) of the cavity. Experimental results show that the proposed monoblock gripper with integrated soft sensors successfully performed real-time grasp detection for power grasping. [ABSTRACT FROM AUTHOR]

This paper proposes a novel soft sensor system for gait phase analysis with its real-time ground reaction force sensing methodology utilizing Recurrent Neural Network. As the full ground contact of human feet is important for natural and stable walking, the soft sensor system embedded in a bendable foot module is required. The sensor system includes four soft sensor units placed in a rubber outsole. Each sensor unit was fabricated using elastic material, and it can measure exerted normal force through a simple capacitive sensing method. In order to measure accurate force with the sensor system, it is important to compensate nonlinearity and hysteresis inherited from elastic material properties. Recurrent Neural Network with time delays is adopted to be a suitable solution for compensating these undesirable characteristics due to its capability to handle the dynamic behavior of sequential data. The sensor units were calibrated based on the training results of Time-Delay Recurrent Neural Networks. ${R}^{2}$ of sensor units is over than 0.998 and RMSE has dropped dramatically by 64%. The feasibility of the sensor system was validated throughout a real-time ground force measuring experiment. Four gait phases were successfully analyzed according to the data obtained. [ABSTRACT FROM AUTHOR]

With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable exoskeletons. However, a large proportion of existing wearable exoskeletons monitor and sense the delivered force and torque through an indirect-measure method, in which the torque is estimated by the motor current. Direct force/torque measuring through low-cost and compact wearable sensors remains an open problem. This paper presents a compact soft sensor system for wearable gait assistance exoskeletons. The contact force is converted into a voltage signal by measuring the air pressure within a soft pneumatic chamber. The developed soft force sensor system was implemented on a robotic hip exoskeleton, and the real-time interaction force between the human thigh and the exoskeleton was measured through two differential soft chambers. The delivered torque of the hip exoskeleton was calculated based on a characterization model. Experimental results suggested that the sensor system achieved direct force measurement with an error of 10.3 ± 6.58%, and torque monitoring for a hip exoskeleton which provided an understanding for the importance of direct force/torque measurement for assistive performance. Compared with traditional rigid force sensors, the proposed system has several merits, as it is compact, low-cost, and has good adaptability to the human body due to the soft structure.

In physical human–machine interaction applications, the accurate measurement of interactive forces between the human and the machine plays a significant role. Sensors for this purpose should not only be accurate and reliable, but also be soft enough to guarantee the safe and compliant interaction. In this aspect, pneumatic sensors with soft air chambers have often been utilized as a soft force sensor system. Although the pneumatic-based force sensor provides a good compliance and softness, however, it measures only a lumped force acting on the chamber, because the pneumatic sensor measures the overall pressure change in the air chamber. In this paper, a three-axis force sensor with three air chambers in a radially symmetric pattern is proposed for the measurement of multi-dimensional interaction forces with high softness and compliance. Each air chamber embeds a pneumatic sensor, and the pressure changes in the three air chambers are measured in order to distinguish the direction of the applied force. By decoupling the sensor signals from the three pneumatic sensors, the three-dimensional force components can be calculated accurately. Consequently, the proposed sensor is able to measure the three-dimensional forces while maintaining the great softness and compliance provided by the soft air chambers. The design, the fabrication method, and the verification of the proposed method are introduced in this paper. [ABSTRACT FROM AUTHOR]

With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable exoskeletons. However, a large proportion of existing wearable exoskeletons monitor and sense the delivered force and torque through an indirect-measure method, in which the torque is estimated by the motor current. Direct force/torque measuring through low-cost and compact wearable sensors remains an open problem. This paper presents a compact soft sensor system for wearable gait assistance exoskeletons. The contact force is converted into a voltage signal by measuring the air pressure within a soft pneumatic chamber. The developed soft force sensor system was implemented on a robotic hip exoskeleton, and the real-time interaction force between the human thigh and the exoskeleton was measured through two differential soft chambers. The delivered torque of the hip exoskeleton was calculated based on a characterization model. Experimental results suggested that the sensor system achieved direct force measurement with an error of 10.3 ± 6.58%, and torque monitoring for a hip exoskeleton which provided an understanding for the importance of direct force/torque measurement for assistive performance. Compared with traditional rigid force sensors, the proposed system has several merits, as it is compact, low-cost, and has good adaptability to the human body due to the soft structure. [ABSTRACT FROM AUTHOR]