Effective early screenings for gastrointestinal diseases are crucial for reducing mortality through timely interventions and improving life expectancy. In this paper, a strain effect-based biomimetic artificial whisker system is proposed to extract the structural and textural information of the tissues in the lumen based on interactive tactile perception data and an end-to-end screening algorithm. Benchmark experiment of the proposed method and an ex-vivo pilot study are conducted to characterize the baseline performance and feasibility of detecting several common tissue structures in surgical application scenarios. Our method shows promising results, with a test accuracy of up to 97.27% and a kappa value of 0.9590. This integrated hardware-software, end-to-end solution is promising to become an emerging human-machine interaction paradigm, empowering traditional healthcare applications.

Accurate robot kinematic modelling is a major component for autonomous robot control to guarantee safety and precision during task execution. In surgical robotics complex robotic structures and actuation mechanisms are generally employed, therefore machine learning techniques can be adopted to build the model of the robot. Probabilistic neural networks are a class of learning approaches that provide information about the uncertainty of the learnt models. In this work we compare two different probabilistic neural networks (Bayesian and Evidential Neural Networks) to model the kinematics of a surgical robotic instrument and propose a control strategy based on Hierarchical Quadratic Programming (HQP) capable of exploiting the model uncertainty to improve the accuracy and safety of the controller. Simulation and real world experiments on different autonomous path tracking tasks show that the model uncertainty highly affects the control performances and prove the effectiveness of the proposed controller in improving task execution.Note to Practitioners—The push towards reducing invasiveness and patient’s traumas in surgery has lead to the requirement of miniaturized and highly articulated robots. This however comes at the cost of having systems that are hard to model and control, which is one of the major limitations for autonomy in surgical robotics. Machine learning has become very effective in modelling complex systems and probabilistic approaches additionally allow estimating the confidence of the learnt model. In robotics field where high precision is required to perform an autonomous task, like in minimally invasive surgery, the robot model needs to be very accurate and controllers need to guarantee safety in performing the desired task, while satisfying additional motion constraints imposed by the application scenario. This work proposes the use of probabilistic neural networks to model the complexity of a surgical robotic instrument and a control strategy capable of ensuring safety by maximizing model’s confidence and guaranteeing satisfaction of imposed motion constraints. In this work a macro-micro manipulator setup is employed, consisting of an articulated surgical robotic instrument connected to a serial-link manipulator. The proposed modelling and control approaches can be used in any other field where controllers need to highly rely on the robot model due to limitations in using external sensors and where leveraging information about model’s confidence can be beneficial. Currently, the work focuses only on pure kinematic modelling and control, thus neglecting any possible interaction with the environment. Future work will focus on addressing this limitation in order to ensure proper force control and effective autonomy.

It is well-known that the tendon-driven robotic manipulator plays an important role in robotic-assisted minimally invasive surgery (MIS). However, due to the intrinsic nonlinearities, uncertainties, slack and hysteresis introduced by the tendon-driven actuation, the tendon-driven robotic manipulator is difficult to model and control when compared with the traditional actuation styles. To serve the modeling purpose, in this paper, the deep-learning-based intelligent modeling of inverse kinematics in the snake-like tendon-driven surgical instrument is presented. In the proposed approach the Deep Recurrent Neural Network (DRNN) with Long Short-Term Memory (LSTM) architecture is adopted to memorize and identify the nonlinear inverse kinematics of the tendon-driven surgical instrument through the history of the motor and tip positions. To collect highly reliable data to train the DRNN, the experiment to generate training data is carefully designed with the consideration of the stainless tendon characters and motor limitations. During the designed controller movements, the kinematics data is obtained by recording the motor positions and the tip positions. Besides, it is noticed that there are correlations of the sequential data samples, which could significantly reduce the modeling accuracy. To remove the correlations and improve the modeling performance, the correlations of the sequential data samples are removed by modifying the training processes. Modeling results and detailed discussions verified the effectiveness of the proposed approach.

Wearable technology in the form of wristwatches, armbands, or fit monitors has fast widespread lately among technology enthusiasts that are eager for a quick hands-on experience with their own body parameters. Nonetheless, the accuracy, replicability and reproducibility of the measurements collected by these monitors is still highly debatable outside laboratory settings, thus resulting in their nonacceptance as valid medical diagnostic tools. Furthermore, the inability to collect temporally detailed physiological variables like heartrate, pulse plethysmography, skin temperature and galvanic skin response for extended periods of time has also been appointed as a factor contributing to wearables’ nonacceptance within the biomedical research community. Even more so if the monitoring is to be performed in remote places, usually involving prolonged and arduous physical tasks performed by the participant. In this paper, we propose an inexpensive prototype smartwatch for prolonged physiological monitoring in remote environments. Equipped with sensing channels that monitor the aforementioned body variables, the device can also be instructed to operate in an asynchronous recording mode, thereby saving battery life and memory while recording some ambient variables (humidity, temperature, luminescence, and atmospheric pressure) in order to provide descriptive context awareness to the physiological processes taking place inside the human body at the same time.

Sensor installation on water infrastructure is challenging due to requirements for service interruption, specialized personnel, regulations and reliability as well as the resultant high costs. Here, a minimally invasive installation method is introduced based on hot-tapping and immersion of a sensor probe. A modular architecture is developed that enables the use of interchangeable multisensor probes, nonspecialist installation and servicing, low-power operation and configurable sensing and connectivity. A prototype implementation with a temperature, pressure, conductivity and flow multisensor probe is presented and tested on an evaluation rig. This article demonstrates simple installation, reliable and accurate sensing capability as well as remote data acquisition. The demonstrated minimally invasive multisensor probes provide an opportunity for the deployment of water quality sensors that typically require immersion, such as pH and spectroscopic composition analysis. This design allows dynamic deployment on existing water infrastructure with expandable sensing capability and minimal interruption, which can be key to addressing important sensing parameters, such as optimal sensor network density and topology.

Monitoring infrastructure offers functional optimization, lower maintenance cost, security, stability, and data analysis benefits. Sensor nodes require some level of energy autonomy for reliable and cost-effective operation, and energy-harvesting methods have been developed in the last two decades for this purpose. Here, a power supply that collects, stores, and delivers regulated power from the stray magnetic field of current-carrying structures is presented. In cm-scale structures the skin effect concentrates current at edges at frequencies even below 1 kHz. A coil-core inductive transducer is designed. A flux-funneling soft magnetic core shape is used, multiplying power density by the square of funneling ratio. A power management circuit combining reactance cancelation, voltage doubling, rectification, supercapacitor storage, and switched inductor voltage boosting and regulation is introduced. The power supply is characterized in house and on a full-size industrial setup, demonstrating a power reception density of 0.36, 0.54, and 0.73 mW/cm3from a 25-A root-mean-square structural current at 360, 500, and 800 Hz, respectively, corresponding to the frequency range of aircraft currents. The regulated output is tested under various loads and cold starting is demonstrated. The introduced method may enable power autonomy to wireless sensors deployed in current-carrying infrastructure.

In minimally invasive robotic surgery, the surgical instrument is usually inserted inside the patient’s body through a small incision, which acts as a remote center of motion (RCM). Serial-link manipulators can be used as macro robots on which microsurgical robotic instruments are mounted to increase the number of degrees of freedom of the system and ensure safe task and RCM motion execution. However, the surgical instrument needs to be placed in an appropriate configuration when completing the motion tasks. The contribution of this article is to present a novel framework that preoperatively identifies the best base configuration, in terms of Roll, Pitch, and Yaw angles, of the microsurgical instrument with respect to the macro serial-link manipulator’s end effector in order to achieve the maximum accuracy and dexterity in performing specified tasks. The framework relies on hierarchical quadratic programming for the control, genetic algorithm for the optimization, and on a resilience to error strategy to make sure deviations from the optimum do not affect the system’s performance. Simulation results show that the mounting configuration of the surgical instrument significantly impacts the performance of the whole macro–micro manipulator in executing the desired motion tasks, and both the simulation and experimental results demonstrate that the proposed optimization method improves the overall performance.

The traditional master-slave teleoperation relies on human expertise without correction mechanisms, resulting in excessive physical and mental workloads. To address these issues, a co-pilot-in-the-loop control framework is investigated for cooperative teleoperation. A deep deterministic policy gradient (DDPG) based agent is realised to effectively restore the master operators' intents without prior knowledge on time delay. The proposed framework allows for introducing an operator (i.e., copilot) to generate commands at the slave side, whose weights are optimally assigned online through DDPG-based arbitration, thereby enhancing the command robustness in the case of possible human operational errors. With the help of interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy identification, force feedback can be reconstructed at the master side without a sense of delay, thus ensuring the telepresence performance in the force-sensor-free scenarios. Two experimental applications validate the effectiveness of the proposed framework.

Robotic dual-arm twisting is a common but very challenging task in both industrial production and daily services, as it often requires dexterous collaboration, a large scale of end-effector rotating, and good adaptivity for object manipulation. Meanwhile, safety and efficiency are primary concerns for robotic dual-arm coordinated manipulation. Thus, the normally adopted fully automated task execution approaches based on environmental perception and motion planning techniques are still inadequate and problematic for the arduous twisting tasks. To this end, this paper presents a novel strategy of the dual-arm coordinated control for twisting manipulation based on the combination of optimized motion planning for one arm and real-time telecontrol with human intelligence for the other. The analysis and simulation results showed it can achieve collision and singularity free for dual arms with enhanced dexterity, safety, and efficiency.

Many industrial-Internet-of-Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles (UAV) and embedded devices with subsystems to facilitate WPT allows a UAV to become a viable mobile power delivery vehicle (PDV) and data collection agent. A key challenge is, therefore, to ensure that a PDV can optimally schedule power delivery across the network, such that it is as reliable and resource efficient as possible. To achieve this and out-perform naive on-demand recharging strategies, in this article, we propose a two-stage wireless power network (WPN) approach in which a large network of devices may be grouped into small clusters, where packets of energy inductively delivered to each cluster by the PDV are acoustically distributed to devices within the cluster. In this article, we describe a novel dynamic recharge scheduling algorithm that combines genetic weighted clustering with nearest neighbor search to jointly minimize PDV travel distance and WPT losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces, and the algorithm is shown to achieve ~ 90% throughput for large, dense networks.

This paper describes the design, implementation and evaluation of a first of its kind cross-layer protocol for wireless communication between flying agents and terrestrial wireless sensors. The protocol is composed of three layers: a new application layer built upon a modified implementation of ContikiMAC over the IEEE 802.15.4 2.4 GHz physical layer. The experimental evaluation shows the protocol to have excellent energy efficiency, low latency and high reliability-approaching 100% for certain parameter settings and operational conditions. The effects of speed, altitude, and direction of approach are also experimentally evaluated, demonstrating that it is of critical importance to take these into account when planning mobile aerial data collection campaigns.

Pervasive sensing-the capability to deploy large numbers of sensors, to link them to communication networks, and to analyze their collective data-is transforming many industries. In mining, networked sensors are already used for remote operation, automation, including driverless vehicles, health, and safety, and exploration. In this paper, the state-of-the-art sensing and monitoring technologies are assessed as solutions against the main challenges and opportunities in the mining industry. Localization, mapping, remote operation, maintenance, and health and safety are identified as the main beneficiaries from rapidly developing technologies, such as 3-D visualization, augmented reality, energy autonomous sensor nodes, distributed sensing, smart network protocols, and big data analytics. It is shown that the identification and management of ore grade, in particular, which transcends each stage of the mining process, may critically benefit from certain arising sensing technologies, where major efficiency improvements are possible in exploration, extraction, haulage, and processing activities.

Body sensor networks are increasingly popular in healthcare, sports, military and security. However, the power supply from conventional batteries is a key bottleneck for the development of body condition monitoring. Energy harvesting from human motion to power wearable or implantable devices is a promising alternative. This paper presents an airflow energy harvester to harness human motion energy from footsteps. An air bladder-turbine energy harvester is designed to convert the footstep motion into electrical energy. The bladders are embedded in shoes to induce airflow from foot-strikes. The turbine is employed to generate electrical energy from airflow. The design parameters of the turbine rotor, including the blade number and the inner diameter of the blades (the diameter of the turbine shaft), were optimized using the computational fluid dynamics (CFD) method. A prototype was developed and tested with footsteps from a 65 kg person. The peak output power of the harvester was first measured for different resistive loads and showed a maximum value of 90.6 mW with a 30.4 Ω load. The harvested energy was then regulated and stored in a power management circuit. 14.8 mJ was stored in the circuit from 165 footsteps, which means 90 μJ was obtained per footstep. The regulated energy was finally used to fully power a fitness tracker which consists of a pedometer and a Bluetooth module. 7.38 mJ was consumed by the tracker per Bluetooth configuration and data transmission. The tracker operated normally with the harvester working continuously.

Motion energy harvesting is a sought after alternative to battery powering for implanted and body worn devices. However, the lack of electricity generation at rest is a major concern. This paper describes a previously presented piezoelectric rotational motion harvester, and presents a mechanism for wireless and external actuation of the main rotor of the device through a magnetic reluctance coupling. With this approach, an internal battery or super-capacitor could be recharged during prolonged periods of inactivity. An improved experimental setup uses a stepper motor to accurately prescribe even high actuation frequencies. A single stack and diametrically opposed dual stacks of driving magnets are investigated. It is demonstrated that adding the additional magnet stack is detrimental to the system performance. Furthermore, the system was tested in a horizontal and a gravity-independent vertical arrangement. Power can successfully be generated regardless of orientation. The maximal separation between driving magnets and harvester reached 20 millimeters. Lastly, the device can operate even under misalignment, and the optimal driving frequency is 25 Hertz, at which over 100 microwatts of power were generated for a device with a functional volume of 1.85 cubic centimeters.

A motion-powered pulse generator using piezoelectric transduction is reported in this paper for wireless sensing devices. A metallic rolling ball is implemented in the prototype as an inertial proof mass excited by external motions at random low frequency. Taking advantage of the metallic proof mass, magnetic coupling can be achieved to actuate the piezoelectric cantilever by attaching tip magnets to the free end. In addition, self-synchronous switching is achieved by applying electrodes to the track of the rolling ball. A new passive prebiasing mechanism is introduced to improve the performance of the pulse generator. Both simulation and experimental results were conducted to demonstrate the improvement. Experimental results show that 76% more energy can be extracted by the prebias mechanism compared to the unbiased case. A transmission circuit based on a Colpitts oscillator was built to test the performance of the capacitor-powered oscillator, which is designed as the load of the pulse generator. By adding a voltage control component, the transmission circuit is capable of encoding a sensor signal by frequency modulation, which demonstrates the feasibility of implementing a motion-powered wireless sensing prototype based on the piezoelectric pulse generator.

This paper presents a piezoelectric pulse generator for body sensing and wireless transmission. A steel rolling ball is used as a proof mass to provide a smooth motion excited by non-harmonic external vibrations. In addition, the proof mass in this prototype is implemented to achieve both impulse excitation of the piezoelectric cantilever by magnetic coupling, and self-synchronous switching. A new actuation mechanism to pre-bias a piezoelectric cantilever passively is demonstrated which enhances the energy extracted. Experimental results are presented to compare between the pre-biased and unbiased cases, showing 76% more extracted energy in the pre-biased case. A transmitter is designed which encodes the sensor signal by frequency modulation, to demonstrate the feasibility of building a self-powered body sensor network (BSN) node based on the demonstrated piezoelectric pulse generator.

This paper presents a piezoelectric rotational energy harvester based on the frequency up-conversion principle and building on the findings from a previously introduced device. The prototype is capable of converting the low frequency and random motion from the human body into a much higher transducer frequency, which increases conversion effectiveness. Design changes, such as using longer and thinner piezoelectric bending beams, have lead to a vast improvement in the achieved output voltage. The beams now experience a clean ring-down after an initial deflection. Furthermore, a custom made linear shaking system for reproducing human motion in a laboratory environment is shown. The results obtained prove successful frequency up-conversion and electrical damping. A maximum power output of 28 micro watts was achieved when the internal rotor went into continuous rotation.

In this paper we discuss a novel approach to delivering wireless power to remote microdevices within Body Sensor/Actuator Networks. With higher energy budgets such devices could extent their functionality from purely diagnostic to therapeutic, and perform such operations as implant mechanical adjustment, drug release, microsurgery, or control of microfluidic valves and pumps. The method is based on ultrasonic power delivery, the novelty being that actuation is powered by ultrasound directly rather than via electrical form. The paper focuses on the main part of the system — a coupled mechanical oscillator driven by acoustic waves — and presents the first experimental results. Several issues related to the biomedical application of the system are also discussed. These include estimating acoustic power levels to avoid adverse bioeffects and tissue damage, as well as studying how the source-receiver misalignment (lateral and angular) affects the system performance.

This paper discusses the operating principle of a rotational energy harvester for body motion with an eccentric proof mass. A mathematical analysis for the rotor motion under different excitations is performed and the gravitational and inertial operation explained. The transducing mechanism works on the principle of frequency up-conversion that is now widely used to harvest low frequency vibration more efficiently, and uses a piezoelectric beam and magnetic coupling. A miniaturized device with an overall size similar to that of a wristwatch is introduced. The fabrication is entirely done using standard milling and turning processes. Experimental results for this device show significant improvement in the attachment of the piezoelectric beam compared to a previous prototype. Furthermore, there is a good match between the magnetic forces and the proof mass for the tested excitations. A disadvantage of the miniaturized prototype is the higher stiffness of the piezo beam, preventing free oscillation after actuation. Modifications to counteract this problem are provided and experimentally validated.

A rotational energy harvester for human body applications is presented in this paper. An oscillating mass, similar to those found in wristwatches is used as a proof mass to act on a piezoelectric impulse excited transduction mechanism that is particularly well suited for these low-frequency, non-harmonic vibrations. The electromechanical coupling is enhanced by letting a piezoelectric beam vibrate at its natural frequency after an initial excitation. The plucking of the beam is achieved by a completely contact less magnetic coupling, beneficial for the longevity of the device. The potential advantages of rotary harvesters are discussed and a first design is introduced. The measurement results demonstrate the successful implementation and make it possible to investigate the influence of different factors on the power output. At a frequency of 2 Hz a maximal power of 2.6 microwatt was achieved when tested on a rocking table.

In this paper we compare two methods of wireless power delivery to implanted microdevices: ultrasonically and via inductive coupling. We build models for both methods and compare them in terms of power transmission efficiency, for different separations and receiver sizes. The simulation results show that at small distances between source and receiver (1 cm) the inductive system outperforms the ultrasonic one (efficiency of 81% vs. 39% for a receiver of 10 mm diameter). At larger distances (10 cm) the efficiencies of both systems reduce significantly, but the ultrasonic system demonstrates much better performance (0.2% vs. 0.013% for a 10 mm receiver). As the receiver gets smaller this gap increases drastically (0.02% vs. 0.02·10-3% for a 2 mm receiver) while the distance after which the ultrasonic system outperforms the inductive one reduces (from 2.9 cm for a 10 mm receiver to 1.5 cm for a 5 mm receiver).

Energy harvesting generators are attractive as inexhaustible replacements for batteries in low-power wireless electronic devices and have received increasing research interest in recent years. Ambient motion is one of the main sources of energy for harvesting, and a wide range of motion-powered energy harvesters have been proposed or demonstrated, particularly at the microscale. This paper reviews the principles and state-of-art in motion-driven miniature energy harvesters and discusses trends, suitable applications, and possible future developments.