Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

Plantar cutaneous feedback plays an important role in stable and efficient gait, by modulating the activity of ankle dorsi-and plantar-flexor muscles. However, central and peripheral nervous trauma often decrease plantar cutaneous feedback and/or interneuronal excitability in processing the plantar cutaneous feedback. In this study, we tested if a fully implantable neural recording and stimulation system could augment plantar cutaneous feedback. Electromyograms were recorded from the medial gastrocnemius muscle for stance phase detection, while biphasic stimulation pulses were applied to the distal-tibial nerve during the stance phase to augment plantar cutaneous feedback. A Bluetooth low energy and a Qi-standard inductive link were adopted for wireless communication and wireless charging, respectively. To test the operation of the system, one intact rat walked on a treadmill with the electrical system implanted into its back. Leg kinematics were recorded to identify the stance phase. Stimulation was applied, with a 250-ms onset delay from stance onset and 200-ms duration, resulting in the onset at 47.58±2.82% of stance phase and the offset at 83.49±4.26% of stance phase (Mean±SEM). The conduction velocity of the compound action potential (31.2 m/s and 41.6 m/s at 1i,zT and 2i,zT, respectively) suggests that the evoked action potential was characteristic of an afferent volley for cutaneous feedback. We also demonstrated successful wireless charging and system reset functions. The experimental results suggest that the presented implantable system can be a valuable neural interface tool to investigate the effect of plantar cutaneous augmentation on gait in a rat model.

Ankle plantarflexion plays an important role in forward propulsion and anterior-posterior balance during locomotion. This component of gait is often critically impacted by neurotraumas and neurological diseases. We hypothesized that augmenting plantar cutaneous feedback, via closed-loop distal-tibial nerve stimulation, could increase ankle plantarflexion during walking. To test the hypothesis, one intact rat walked on a motorized treadmill with implanted electronic device and electrodes for closed-loop neural recording and stimulation. Constant-current biphasic electrical pulse train was applied to distal-tibial nerve, based on electromyogram recorded from the medial gastrocnemius muscle, to be timed with the stance phase. The stimulation current threshold to evoke plantar cutaneous feedback was set at 30 μA (1·T), based on compound action potential evoked by stimulation. The maximum ankle joint angle at plantarflexion, during the application of stimulation currents of 3.3·T and 6.6·T, respectively, was increased from 149.4° (baseline) to 165.4° and 161.6°. The minimum ankle joint angle at dorsiflexion was decreased from 59.4° (baseline) to 53.1°, during the application of stimulation currents of 3.3·T, but not changed by 6.6·T. Plantar cutaneous augmentation also changed other gait kinematic parameters. Stance duty factor was increased from 51.9% (baseline) to 65.7% and 64.0%, respectively, by 3.3·T and 6.6·T, primarily due to a decrease in swing duration. Cycle duration was consistently decreased by the stimulation. In the control trial after two stimulation trials, a strong after-effect was detected in overall gait kinematics as well as ankle plantarflexion, suggesting that this stimulation has the potential for producing long-term changes in gait kinematics.

(1) Background: Insects, which serve as model systems for many disciplines with their unique advantages, have not been extensively studied in gait research because of the lack of appropriate tools and insect models to properly study the insect gaits. (2) Methods: In this study, we present a gait analysis of grasshoppers with a closed-loop custom-designed motorized insect treadmill with an optical recording system for quantitative gait analysis. We used the eastern lubber grasshopper, a flightless and large-bodied species, as our insect model. Gait kinematics were recorded and analyzed by making three grasshoppers walk on the treadmill with various speeds from 0.1 to 1.5 m/s. (3) Results: Stance duty factor was measured as 70–95% and decreased as walking speed increased. As the walking speed increased, the number of contact legs decreased, and diagonal arrangement of contact was observed at walking speed of 1.1 cm/s. (4) Conclusions: This pilot study of gait analysis of grasshoppers using the custom-designed motorized insect treadmill with the optical recording system demonstrates the feasibility of quantitative, repeatable, and real-time insect gait analysis.

Real-time detection of gait phase is a critical challenge for closed-loop neuromodulation systems aimed at restoring locomotion after spinal cord injury (SCI). However, many existing gait analysis approaches rely on offline processing or computationally intensive models that are unsuitable for low-latency, embedded deployment. In this study, we present a hybrid AI-based sensing architecture that enables real-time kinematic extraction and on-device gait-phase classification for closed-loop neuromodulation in SCI mice. A vision AI module performs marker-assisted, high-speed pose estimation to extract hindlimb joint angles during treadmill locomotion, while a lightweight edge AI model deployed on a microcontroller classifies gait phase and generates real-time phase-dependent stimulation triggers for closed-loop neuromodulation. The integrated system generalized to unseen SCI gait patterns without injury-specific retraining and enabled precise phase-locked biphasic stimulation in a bench-top closed-loop evaluation. This work demonstrates a low-latency, attachment-free sensing and control framework for gait-responsive neuromodulation, supporting future translation to wearable or implantable closed-loop neurorehabilitation systems.

Real-time detection of gait phase is a critical challenge for closed-loop neuromodulation systems aimed at restoring locomotion after spinal cord injury (SCI). However, many existing gait analysis approaches rely on offline processing or computationally intensive models that are unsuitable for low-latency, embedded deployment. In this study, we present a hybrid AI-based sensing architecture that enables real-time kinematic extraction and on-device gait phase classification for closed-loop neuromodulation in SCI mice. A vision AI module performs marker-assisted, high-speed pose estimation to extract hindlimb joint angles during treadmill locomotion, while a lightweight edge AI model deployed on a microcontroller classifies gait phase and generates real-time phase-dependent stimulation triggers for closed-loop neuromodulation. The integrated system generalized to unseen SCI gait patterns without injury-specific retraining and enabled precise phase-locked biphasic stimulation in a bench-top closed-loop evaluation. This work demonstrates a low-latency, attachment-free sensing and control framework for gait-responsive neuromodulation, supporting future translation to wearable or implantable closed-loop neurorehabilitation systems.