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This paper presents a three-axis inductive tactile force sensor utilizing a magnetorheological elastomer (MRE) marker for inducing inductance change in square-shaped coils, in response to an input force. The sensor consists of copper coils on a printed circuit board (PCB) and a patterned elastomer layer between the marker and coils. The proposed configuration of the coils reduces the mechanical cross-talk between the applied three-axis forces, thus decoupling the force axes. Finite element method (FEM) based analyses are carried out to optimize the sensing gap between the coils and marker. To observe the effect of the elastomer material on the sensor performance, two types of silicone rubbers, Ecoflex-30 and RTV-528 are used to make the patterned elastomer and the marker. The sensor with Ecoflex-30 elastomer can measure forces up to 25 N in normal and 2.5 N in shear directions whereas the sensor with RTV-528 elastomer has a higher force range of up to 30 N for normal and 6 N for shear directions but has a lower sensitivity. A resolution of 12.71 mN, 7.96 mN and 6.61 mN is achieved with Ecoflex-30 whereas for RTV-528 the force resolution is 76.52 mN, 11.7 mN and 23.98 mN for normal, shear and angular shear directions respectively. The hysteresis error for the proposed sensors is 7.2% and 5.3% for Ecoflex-30 and RTV-528 elastomers, respectively. The proposed inductive tactile force sensor can be used in a wide range of robotic surgical applications for sensing multi-directional forces with better resolution and low hysteresis error.

Amputees with lower limb loss need special care during daily life activities to make the movement natural as before amputation. No such work exists covering the main aspects from causes of amputation to the psycho-social impact of the amputees after using the prosthetic device. This review presents for lower limb prosthesis; the study of lower limb amputation, design & development, control strategies & machine learning algorithms, the psycho-social impact of prosthetic users, and design trends in patents. Research articles, review papers, magazines, letters, study reports, surveys, and patents, etc. have been used as sources for this review. Traumatic injuries and different diseases have been found as common causes of amputation. Design & development section illustrates design mechanisms, the categories of passive, active, & semi-active prostheses, an overview of a subset of commercially available prosthetic devices, and 3D printing of the accessories. The control section provides information about control techniques, sensors used, machine learning algorithms, and their key outcomes. Quality of life, phantom limb pain, and psycho-social impact of prosthetic users have been summarized for different countries that are believed to attract the interest of the readers. We have also developed an open-source database “FAKH-50” for patents to emphasize the design trends and advancements in lower limb prostheses from 1970 to 2020. Overall trend analysis determined is in the descending order as the knee (48%) > ankle (28%) > foot (22%) > hip (2%) patents in the current version of our database. The forthcoming section highlights the challenges and prospects of the domain. A mutual observation demands the design of a bio-compatible, lightweight, and economic prosthesis to track the normal human gait by eliminating phantom limb pain. This will empower the amputees to live a quality life in society. This work may be beneficial for researchers, technicians, clinicians, and amputees.

Weeds affects crops health as it shares water and nutrients from the soil, as a result it decreases crop yield. Manual weedicide spray through bag-pack is hazardous to human health. Localized autonomous weedicide spray through aerial spraying units can help save water, weedicide chemical and effect less on human health. Such systems require multi-spectral cues to classify crop, weed, and soil surface. Our focus in this paper is on the detection of weeds in the sugar beet crop, using air-borne multispectral camera sensors, which is considered as an alternative crop to sugarcane to obtain sugar in Pakistan. We developed a new framework for weed identification; a patch-based classification approach as appose to semantic segmentation that is more realistic for real-time intelligent aerial spraying systems. Our approach converts 3-class pixel classification problem into a 2-class crop-weed patch classification problem which in turns improves crop and weed classification accuracy. For classification, we developed a new VGG-Beet convolutional neural network (CNN), which is based on generic VGG16 (visual graphics group) CNN model with 11 convolutional layers. For experiments, we captured a sugar beet dataset with 3-channel multispectral sensor with a ground sampling distance (GSD) of 0.2 cm/pixel and a height of 4 meters. For better comparison, we used two publicly available sugar beet crop aerial imagery datasets, captured using a 5-channel multispectral sensor and a 4-Channel multispectral sensor with a ground sampling distance of 1cm and a height of 10 meters. We observed that patch-based method is more robust to different lighting conditions. To produce low cost weed detection system usage of Agrocam sensor is recommended, for higher accuracy Red Edge and Sequoia multispectral sensors with more channels should be deployed. We observed higher crop-weed accuracy and lower testing time for our patch-based approach as compared to U-Net and Deeplab based semantic segmentation networks.