We survey the state of the art in a variety of force sensors for designing and application of robotic hand. Most of the force sensors are examined based on tactile sensing. For a decade, many papers have widely discussed various sensor technologies and transducer methods which are based on microelectromechanical system (MEMS) and silicon used for improving the accuracy and performance measurement of tactile sensing capabilities especially for robotic hand applications. We found that transducers and materials such as piezoresistive and polymer, respectively, are used in order to improve the sensing sensitivity for grasping mechanisms in future. This predicted growth in such applications will explode into high risk tasks which requires very precise purposes. It shows considerable potential and significant levels of research attention.
Most caregivers have low back pain which results from frequent care activities such as assistance motion that supports transfer and standing-up. Various parameters are associated with the caregiver’s lumbar load. In this study, we focus on the foot position of the caregiver as one of the subjective adjustable parameters. This study aimed to analyze the relationship between foot position and stresses of the L4–L5 joint as lumbar load during supporting standing-up via musculoskeletal simulation. The musculoskeletal model was tasked with simulating supported standing-up motions based on a specific pelvic position and angular variation of each joint. The anterior foot (left foot) was fixed, and the posterior foot (right foot) was moved to three backward positions and three rightward positions, thus obtaining nine posterior foot positions. Compressive, anteroposterior shear, and lateral shear stresses of the L4–L5 joint were compared for nine foot positions. The results showed that as the anteroposterior distance and lateral widths between both feet increased, the average value of compressive/shear stress of the L4–L5 joint during motions decreased. From our findings, we hypothesized that the foot position may reduce the lumbar load and prevent low back pain.
This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.
[Purpose] In caregivers, low load posture is necessary to prevent lower back pain during patient handling activities such as sit-to-stand support. This study focused on the foot-position of caregivers as an adjustable and useful parameter. A wide stance decreases the stress on the lumbar vertebra. However, this foot-position increases loading of the spinae erector muscles. The aim of this study was to investigate the relationship of anterior-posterior and lateral-medial distances between feet and activity of the spinae erector muscles to determine the optimal foot-position for reducing stress on the lumbar vertebra without increasing spinae erector muscle load. [Participants and Methods] Five young male participants were asked to provide sit-to-stand support 10 times using nine normalized foot-positions with different anterior-posterior and lateral-medial distances. Surface electromyograms of the erector spinae and lower limb muscles were measured during sit-to-stand support. [Results] The results showed that the optimal foot-position (anterior-posterior 55%, lateral-medial 20% of body height) increased muscle activity within the lower limb muscles compared with the lower back muscles and did not increase loads on the erector spinae muscle. [Conclusion] Optimizing foot-position can reduce stress on the lumbar vertebra without increasing load on the spinae erector muscles.
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