For people with amputated lower limbs, it is imperative to make high-performance prostheses that reproduce, as accurately as possible, the functions of the amputated limb. In this case, a preliminary study of the lower limbs from a kinematic and dynamic point of view is necessary. This paper proposes a prosthesis design and a system for acquiring the information needed to determine the stepping phase kinematic and dynamic parameters of the legs. This system consists of a sensory system attached to the legs and a acquisition data unit built around a microcontroller. The sensory system is based on a sensory system for determining the weight distribution on the sole, made of resistive pressure sensors. The sensory system will be subjected to measurement repeatability and homogeneity tests to evaluate and validate the accuracy and error of the proposed solution. The data obtained by the sensory system is transmitted in real-time, via wi-fi, to a computer system for interpretation. After processing and interpreting the data using standard data sets for comparison, the position of the legs, the type of gait and the phase of movement can be determined. Constructively, the system is configurable and can be adapted to any person, male or female, regardless of shoe size.

In recent decades, the field of physical rehabilitation, with the help of robotic systems that aid the population of any age with locomotor difficulties, has been evolving rapidly. Several robotic exoskeleton systems of the lower limbs have been proposed in the patent literature and some are even commercially available. Given the above, we are asking ourselves at the end of the COVID-19 pandemic: how much has this pandemic affected both the publication of patents and the application of new ones? How has new patents’ publication volume or application in robotic exoskeleton systems changed? We hypothesize that this pandemic has caused a reduction in the volume of new applications and possibly publications. We compare pandemic analysis and the last decade’s analysis to answer these questions. In this study, we used a set of statistical tests to see if there were any statistically significant changes. Our results show that the pandemic had at least one effect on applying for new patents based on the information analyzed from the three databases examined.

We introduce Experience-Based Pattern Matching (EBPM) as a biologically grounded alternative to Predictive Coding (PC) for rapid recognition and action. EBPM posits that familiar inputs trigger direct reactivation of multimodal engrams (sensory–motor–interoceptive–cognitive), avoiding continuous generative simulation and thereby reducing latency and compute. We provide a method-by-method comparison between EBPM and adjacent frameworks—attractor/Hopfield models, Complementary Learning Systems, hippocampal indexing (pattern completion/separation), HTM/sequence memory, Sparse Distributed Memory, memory-augmented neural networks (NTM/DNC), episodic control, metric-learning/prototypical networks, and PC/Active Inference—explicitly identifying (i) shared mechanisms, (ii) additions brought by each prior theory, and (iii) what EBPM adds beyond them. We further present DIME (Dynamic Integrative Matching and Encoding), a hybrid architecture that exploits EBPM’s one-shot recall on familiar inputs and falls back to PC under uncertainty or novelty via a lightweight controller. Across ANN benchmarks (MNIST, Fashion-MNIST, CIFAR-10) and a virtual robotics obstacle-course, EBPM consistently yields the lowest inference latency on familiar inputs, PC remains more robust to noise/novelty, and DIME adapts between them without sacrificing task performance. Finally, we formalize Algorithm 1 (EBPM) and map each step to prior art (sparse coding and assemblies; lateral inhibition/WTA; attractor-like selection; salience/LC-NE; Hebbian/STDP; hippocampal indexing), clarifying how EBPM integrates classic mechanisms into a unified, multimodal engram framework. The results motivate EBPM/DIME as neuro-plausible, energy-aware blueprints for hybrid AI and real-time robotic control.

Predictive Coding (PC) frameworks claim to model recognition via prediction–error loops, but they often lack explicit biological implementation of fast familiar recognition and impose latency that limits real-time robotic control. We begin with Experience-Based Pattern Matching (EBPM), a biologically grounded mechanism inspired by neural engram reactivation, enabling near-instantaneous recognition of familiar stimuli without iterative inference. Building upon this, we propose Dynamic Integrative Matching and Encoding (DIME), a hybrid system that relies on EBPM under familiar and low-uncertainty conditions and dynamically engages PC when confronted with novelty or high uncertainty. We evaluate EBPM, PC, and DIME across multiple image datasets (MNIST, Fashion-MNIST, CIFAR-10) and on a robotic obstacle-course simulation. Results from multi-seed experiments with ablation and complexity analyses show that EBPM achieves minimal latency (e.g., ~0.03 ms/ex in MNIST, ~0.026 ms/step in robotics) but poor performance in novel or noisy cases; PC exhibits robustness at a high cost; DIME delivers strong trade-offs—boosted accuracy in familiar clean situations (+4–5% over EBPM on CIFAR-10), while cutting PC invocations by ~50% relative to pure PC. Our contributions: (i) formalizing EBPM as a neurocomputational algorithm built from biologically plausible principles, (ii) developing DIME as a dynamic EBPM–PC integrator, (iii) providing ablation and complexity analyses illuminating component roles, and (iv) offering empirical validation in both perceptual and embodied robotic scenarios—paving the way for low-latency recognition systems.

This paper presents a new conception and analyzes a hyperredundant continuous robot (continuous style manipulator), drive system, and control strategy. The robot includes ten flexible segments and can be extended to several components as needed. The chosen hyper-redundant robot has a continuous infinite hybrid structure (HHRIC), based on hydraulic control with a rheological element. This system combines the advantage of a joint-level drive with a lightweight construction similar to the base-driven robots. It is suitable for tasks such as wiring in hard-toreach areas (caves, subaccounts, steep areas), transportation of fluids or food to areas affected by natural disasters (people buried under ruins), exploration in difficult areas (speleological research). Generally, the control algorithms for hyper-redundant robots are specific to the robots’ constructive particularities to which they have applied and the environment in which they operate. Experimental results validate the proposal robot design and control strategies in virtual reality. As a result, it is concluded that hyper-redundant robots and immersive technologies should play an essential role soon in automated and teleoperation applications.