Wireless Powered Microwave-Light Conversion Platform with Dual-Stimulus Nanoresponder Coating for Deep-Seated Photodynamic Therapy.

Traditional external field-assisted therapies, e.g., microwave (MW) therapy and phototherapy, cannot effectively and minimally damage eliminate deep-seated infection, owing to the poor penetrability of light and low reactive oxygen species (ROS) stimulation capability of MW. Herein, an implantable and wireless-powered therapeutic platform (CNT-FeTHQ-TS), in which external MW can be converted into internal light via MW wireless-powered light-emitting chips, is designed to eradicate deep-seated tissue infections by MW-induced deep-seated photodynamic therapy. In application, CNT-FeTHQ-TS is implanted at internal lesions, and the chip emits light under external MW irradiation. Subsequently, CNT-FeTHQ coating in the platform can respond to both MW and light simultaneously to generate ROS and MW-hyperthermia for rapid and precise sterilization at focus. Importantly, MW also improves the photodynamic performance of CNT-FeTHQ by introducing vacancies in FeTHQ to facilitate the photoexcitation process and changing the spin state of electrons to inhibit the complexation of photogenerated electron-hole pairs, which were confirmed by simulation calculations and in situ MW-irradiated photoluminescence experiments. In vivo, CNT-FeTHQ-TS can effectively cure mice with Staphylococcus aureus infection in dorsal subcutaneous tissue. This work overcomes the key clinical limitations of safe energy transmission and conversion for treating deep-seated infections.

A compact co-aperture dual-sense circularly polarized antenna for simultaneous transmit and receive systems.

This paper proposes a compact design of dual-sense circularly polarized (CP) antenna for simultaneous transmit (Tx) and receive (Rx) communication systems. The primary radiating aperture of the proposed antenna is a 2 × 2 unit-cell metasurface (MS). The MS is excited by the asymmetric patch in the center, which acts as the CP source of the whole antenna structure. By properly tuning the feeding positions, dual-sense CP with high isolation can be achieved. For verification, an antenna prototype with compact dimensions of 0.36λ × 0.36λ × 0.02λ (λ is the free-space wavelength at the center operating frequency) is fabricated and measured. The measured operating bandwidth is 1.6% (2.45-2.49 GHz), in which the reflection and transmission coefficients are less than-10 dB and the axial ratio is lower than 3 dB. Within this band, the maximum isolation value is 39 dB, and the peak gain is 5.7 dBi.

Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.

The provision of Wireless Fidelity (Wi-Fi) service in an indoor environment is a crucial task and the decay in signal strength issues arises especially in indoor environments. The Line-of-Sight (LOS) is a path for signal propagation that commonly impedes innumerable indoor objects damage signals and also causes signal fading. In addition, the Signal decay (signal penetration), signal reflection, and long transmission distance between transceivers are the key concerns. The signals lose their power due to the existence of obstacles (path of signals) and hence destroy received signal strength (RSS) between different communicating nodes and ultimately cause loss of the packet. Thus, to solve this issue, herein we propose an advanced model to maximize the LOS in communicating nodes using a modern indoor environment. Our proposal comprised various components for instance signal enhancers, repeaters, reflectors,. these components are connected. The signal attenuation and calculation model comprises of power algorithm and hence it can quickly and efficiently find the walls and corridors as obstacles in an indoor environment. We compared our proposed model with state of the art model using Received Signal Strength (RSS) and Packet Delivery Ratio (PDR) (different scenario) and found that our proposed model is efficient. Our proposed model achieved high network throughput as compared to the state-of-the-art models.

Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security.

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments.

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)-electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids.

Functional nanotransducer-mediated wireless neural modulation techniques.

Functional nanomaterials have emerged as versatile nanotransducers for wireless neural modulation because of their minimal invasion and high spatiotemporal resolution. The nanotransducers can convert external excitation sources (e.g. NIR light, x-rays, and magnetic fields) to visible light (or local heat) to activate optogenetic opsins and thermosensitive ion channels for neuromodulation. The present review provides insights into the fundamentals of the mostly used functional nanomaterials in wireless neuromodulation including upconversion nanoparticles, nanoscintillators, and magnetic nanoparticles. We further discussed the recent developments in design strategies of functional nanomaterials with enhanced energy conversion performance that have greatly expanded the field of neuromodulation. We summarized the applications of functional nanomaterials-mediated wireless neuromodulation techniques, including exciting/silencing neurons, modulating brain activity, controlling motor behaviors, and regulating peripheral organ function in mice. Finally, we discussed some key considerations in functional nanotransducer-mediated wireless neuromodulation along with the current challenges and future directions.

Eco-Friendly Textile-Based Wearable Humidity Sensor with Multinode Wireless Connectivity for Healthcare Applications.

Textile-based wearable humidity sensors are of great interest for human healthcare monitoring as they can provide critical human-physiology information. The demand for wearable and sustainable sensing technology has significantly promoted the development of eco-friendly sensing solutions for potential real-world applications. Herein, a biodegradable cotton (textile)-based wearable humidity sensor has been developed using fabsil-treated cotton fabric coated with a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) sensing layer. The structural, chemical composition, hygroscopicity, and morphological properties are examined using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), contact angle measurement, and scanning electron microscopy (SEM) analysis. The developed sensor exhibited a nearly linear response (Adj. R-square value observed as 0.95035) over a broad relative humidity (RH) range from 25 to 91.5%RH displaying high sensitivity (26.1%/%RH). The sensor shows excellent reproducibility (on replica sensors with a margin of error ±1.98%) and appreciable stability/aging with time (>4.5 months), high flexibility (studied at bending angles 30°, 70°, 120°, and 150°), substantial response/recovery durations (suitable for multiple applications), and highly repeatable (multicyclic analysis) sensing performance. The prospective relevance of the developed humidity sensor toward healthcare applications is demonstrated via breathing rate monitoring (via a sensor attached to a face mask), distinguishing different breathing patterns (normal, deep, and fast), skin moisture monitoring, and neonatal care (diaper wetting). The multinode wireless connectivity is demonstrated using a Raspberry Pi Pico-based system for demonstrating the potential applicability of the developed sensor as a real-time humidity monitoring system for the healthcare sector. Further, the biodegradability analysis of the used textile is evaluated using the soil burial degradation test. The work suggests the potential applicability of the developed flexible and eco-friendly humidity sensor in wearable healthcare devices and other humidity sensing applications.

Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags.

To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3-5 m.

Security Analysis for Smart Healthcare Systems.

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.

Design and Evaluation of a Low-Power Wide-Area Network (LPWAN)-Based Emergency Response System for Individuals with Special Needs in Smart Buildings.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.

Biocompatible Electrical and Optical Interfaces for Implantable Sensors and Devices.

Implantable bioelectronics hold tremendous potential in the field of healthcare, yet the performance of these systems heavily relies on the interfaces between artificial machines and living tissues. In this paper, we discuss the recent developments of tethered interfaces, as well as those of non-tethered interfaces. Among them, systems that study neural activity receive significant attention due to their innovative developments and high relevance in contemporary research, but other functional types of interface systems are also explored to provide a comprehensive overview of the field. We also analyze the key considerations, including perforation site selection, fixing strategies, long-term retention, and wireless communication, highlighting the challenges and opportunities with stable, effective, and biocompatible interfaces. Furthermore, we propose a primitive model of biocompatible electrical and optical interfaces for implantable systems, which simultaneously possesses biocompatibility, stability, and convenience. Finally, we point out the future directions of interfacing strategies.

Epidemiological exploration of the impact of bluetooth headset usage on thyroid nodules using Shapley additive explanations method.

With an increasing prevalence of thyroid nodules globally, this study investigates the potential correlation between the use of Bluetooth headsets and the incidence of thyroid nodules, considering the cumulative effects of non-ionizing radiation (NIR) emitted by these devices. In this study, we analyzed 600 valid questionnaires from the WenJuanXing platform using Propensity Score Matching (PSM) and the XGBOOST model, supplemented by SHAP analysis, to assess the risk of thyroid nodules. PSM was utilized to balance baseline characteristic differences, thereby reducing bias. The XGBOOST model was then employed to predict risk factors, with model efficacy measured by the area under the Receiver Operating Characteristic (ROC) curve (AUC). SHAP analysis helped quantify and explain the impact of each feature on the prediction outcomes, identifying key risk factors. Initially, 600 valid questionnaires from the WenJuanXing platform underwent PSM processing, resulting in a matched dataset of 96 cases for modeling analysis. The AUC value of the XGBOOST model reached 0.95, demonstrating high accuracy in differentiating thyroid nodule risks. SHAP analysis revealed age and daily Bluetooth headset usage duration as the two most significant factors affecting thyroid nodule risk. Specifically, longer daily usage durations of Bluetooth headsets were strongly linked to an increased risk of developing thyroid nodules, as indicated by the SHAP analysis outcomes. Our study highlighted a significant impact relationship between prolonged Bluetooth headset use and increased thyroid nodule risk, emphasizing the importance of considering health impacts in the use of modern technology, especially for devices like Bluetooth headsets that are frequently used daily. Through precise model predictions and variable importance analysis, our research provides a scientific basis for the formulation of public health policies and personal health habit choices, suggesting that attention should be paid to the duration of Bluetooth headset use in daily life to reduce the potential risk of thyroid nodules. Future research should further investigate the biological mechanisms of this relationship and consider additional potential influencing factors to offer more comprehensive health guidance and preventive measures.

An intelligent algorithm for energy efficiency optimization in software-defined wireless sensor networks for 5G communications.

Wireless communications have lately experienced substantial exploitation because they provide a lot of flexibility for data delivery. It provides connection and mobility by using air as a medium. Wireless sensor networks (WSN) are now the most popular wireless technologies. They need a communication infrastructure that is both energy and computationally efficient, which is made feasible by developing the best communication protocol algorithms. The internet of things (IoT) paradigm is anticipated to be heavily reliant on a networking architecture that is currently in development and dubbed software-defined WSN. Energy-efficient routing design is a key objective for WSNs. Cluster routing is one of the most commonly used routing techniques for extending network life. This research proposes a novel approach for increasing the energy effectiveness and longevity of software-defined WSNs. The major goal is to reduce the energy consumption of the cluster routing protocol using the firefly algorithm and high-efficiency entropy. According to the findings of the simulation, the suggested method outperforms existing algorithms in terms of system performance under various operating conditions. The number of alive nodes determined by the proposed algorithm is about 42.06% higher than Distributed Energy-Efficient Clustering with firefly algorithm (DEEC-FA) and 13.95% higher than Improved Firefly Clustering IFCEER and 12.05% higher than another referenced algorithm.

8-port MIMO antenna at 27 GHz for n261 band and exploring for body centric communication.

This paper presents a compact 5G wideband antenna designed for body-centric networks (BCN. The single element antenna design includes a simple T-shaped radiator patch with ring shaped ground plane and transformer impedance feedline. First, the antenna was simulated in free-space, and its resonant frequency is found to be 27 GHz, falling within 5G's n261 band. The proposed single radiator antenna has a size of 23.375 mm3, and it offers a wide impedance bandwidth of 2.0 GHz (26-28 GHz). Parametric studies demonstrated that by increasing the length of slots in patch, the antenna frequency can be reduced further. Single radiator antenna is used as 8-element MIMO structure. Parallel adjacent antenna in X-direction has minimal coupling effect, whereas antenna placed in Y-direction has high coupling effect. Thus, coupling is reduced by etching a wall of slots in ground plane. It alters the surface current interference in Y-direction and limits the coupling effect. The antenna is investigated to use in body area network applications. To evaluate its on-body performance, an equivalent body model is virtually developed. The on-body performance is assessed by placing the antenna in close proximity to body model. Stable and robust performance is achieved for the on-body operation. At the resonant point, the antenna exhibits a reflection coefficient of -30 dB (free space) and -40 dB (on-body), high isolation of above 20 dB between adjacent radiators and above 30 dB for other radiators. Antenna has stable performance for different body tissues and on the non-planar structures. Bidirectional radiation pattern with gain of 2.53 dB and broadside type orientations with gain of 4.64 dB are achieved for free space and on body operations respectively. low specific absorption rate makes antenna safe for health care devices. Further, diversity performance is measured in terms of envelope correlation coefficient (ECC), and diversity gain (DG). Maximum Value of ECC is 0.005 and minimum value DG is 9.97 at 27 GHz which confirms the excellence of antenna for MIMO applications.

Injectable ultrasonic sensor for wireless monitoring of intracranial signals.

Direct and precise monitoring of intracranial physiology holds immense importance in delineating injuries, prognostication and averting disease1. Wired clinical instruments that use percutaneous leads are accurate but are susceptible to infection, patient mobility constraints and potential surgical complications during removal2. Wireless implantable devices provide greater operational freedom but include issues such as limited detection range, poor degradation and difficulty in size reduction in the human body3. Here we present an injectable, bioresorbable and wireless metastructured hydrogel (metagel) sensor for ultrasonic monitoring of intracranial signals. The metagel sensors are cubes 2 × 2 × 2 mm3 in size that encompass both biodegradable and stimulus-responsive hydrogels and periodically aligned air columns with a specific acoustic reflection spectrum. Implanted into intracranial space with a puncture needle, the metagel deforms in response to physiological environmental changes, causing peak frequency shifts of reflected ultrasound waves that can be wirelessly measured by an external ultrasound probe. The metagel sensor can independently detect intracranial pressure, temperature, pH and flow rate, realize a detection depth of 10 cm and almost fully degrade within 18 weeks. Animal experiments on rats and pigs indicate promising multiparametric sensing performances on a par with conventional non-resorbable wired clinical benchmarks.

Wearable, battery-free, wireless multiplexed printed sensors for heat stroke prevention with mussel-inspired bio-adhesive membranes.

Wearable technologies are becoming pervasive in our society, and their development continues to accelerate the untapped potential of continuous and ubiquitous sensing, coupled with big data analysis and interpretation, has only just begun to unfold. However, existing wearable devices are still bulky (mainly due to batteries and electronics) and have suboptimal skin contact. In this work, we propose a novel approach based on a sensor network produced through inkjet printing of nanofunctional inks onto a semipermeable substrate. This network enables real-time monitoring of critical physiological parameters, including temperature, humidity, and muscle contraction. Remarkably, our system operates under battery-free and wireless near-field communication (NFC) technology for data readout via smartphones. Moreover, two of the three sensors were integrated onto a naturally adhesive bioinspired membrane. This membrane, developed using an eco-friendly, high-throughput process, draws inspiration from the remarkable adhesive properties of mussel-inspired molecules. The resulting ultra-conformable membrane adheres effortlessly to the skin, ensuring reliable and continuous data collection. The urgency of effective monitoring systems cannot be overstated, especially in the context of rising heat stroke incidents attributed to climate change and high-risk occupations. Heat stroke manifests as elevated skin temperature, lack of sweating, and seizures. Swift intervention is crucial to prevent progression to coma or fatality. Therefore, our proposed system holds immense promise for the monitoring of these parameters on the field, benefiting both the general population and high-risk workers, such as firefighters.

Compact In Situ Electrochemical NMR with Wireless and Anti-interference Strategy in Multiscenario Applications.

The integration of electrochemistry with nuclear magnetic resonance (NMR) spectroscopy recently offers a powerful approach to understanding oxidative metabolism, detecting reactive intermediates, and predicting biological activities. This combination is particularly effective as electrochemical methods provide excellent mimics of metabolic processes, while NMR spectroscopy offers precise chemical analysis. NMR is already widely utilized in the quality control of pharmaceuticals, foods, and additives and in metabolomic studies. However, the introduction of additional and external connections into the magnet has posed challenges, leading to signal deterioration and limitations in routine measurements. Herein, we report an anti-interference compact in situ electrochemical NMR system (AICISENS). Through a wireless strategy, the compact design allows for the independent and stable operation of electrochemical NMR components with effective interference isolation. Thus, it opens an avenue toward easy integration into in situ platforms, applicable not only to laboratory settings but also to fieldwork. The operability, reliability, and versatility were validated with a series of biomimetic assessments, including measurements of microbial electrochemical systems, functional foods, and simulated drug metabolisms. The robust performance of AICISENS demonstrates its high potential as a powerful analytical tool across diverse applications.

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring.

Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R2 of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.

Design of a Compact Circularly Polarized Implantable Antenna for Capsule Endoscopy Systems.

This research proposes a miniature circular polarization antenna used in a wireless capsule endoscopy system at 2.45 GHz for industrial, scientific, and medical bands. We propose a method of cutting a chamfer rectangular slot on a circular radiation patch and introducing a curved radiation structure into the centerline position of the chamfer rectangular slot, while a short-circuit probe is added to achieve miniaturization. Therefore, we significantly reduced the size of the antenna and made it exhibit circularly polarized radiation characteristics. A cross-slot is cut in the GND to enable the antenna to better cover the operating band while being able to meet the complex human environment. The effective axis ratio bandwidth is 120 MHz (2.38-2.50 GHz). Its size is π × 0.032λ02 × 0.007λ0 (where λ0 is the free-space wavelength of at 2.4 GHz). In addition, the effect of different organs such as muscle, stomach, small intestine, and big intestine on the antenna when it was embedded into the wireless capsule endoscopy (WCE) system was further discussed, and the results proved that the WCE system has better robustness in different organs. The antenna's specific absorption rate can follow the IEEE Standard Safety Guidelines (IEEE C95.1-1999). A prototype is fabricated and measured. The experimental results are consistent with the simulation results.

Biosymbiotic haptic feedback - Sustained long term human machine interfaces.

Haptic technology permeates diverse fields and is receiving renewed attention for VR and AR applications. Advances in flexible electronics, facilitate the integration of haptic technologies into soft wearable systems, however, because of small footprint requirements face challenges of operational time requiring either large batteries, wired connections or frequent recharge, restricting the utility of haptic devices to short-duration tasks or low duty cycles, prohibiting continuously assisting applications. Currently many chronic applications are not investigated because of this technological gap. Here, we address wireless power and operation challenges with a biosymbiotic approach enabling continuous operation without user intervention, facilitated by wireless power transfer, eliminating the need for large batteries, and offering long-term haptic feedback without adhesive attachment to the body. These capabilities enable haptic feedback for robotic surgery training and posture correction over weeks of use with neural net computation. The demonstrations showcase that this device class expands use beyond conventional brick and strap or epidermally attached devices enabling new fields of use for imperceptible therapeutic and assistive haptic technologies supporting care and disease management.