Sensor Array Research Articles

Soft Monolithic Shielded Sensors to Measure Shear and Normal Forces for Local Slip Detection

AbstractSoft force sensors enable closed loop control or autonomous operation of compliant robot systems. A stretchable force sensing surface containing an array of independent sensor units is reported. This design allows for the measurement of local shear and normal forces at multiple locations, independently, providing real‐time, multi‐axis force information at each unit. The sensor array is based on capacitive strain sensing, using liquid metal electrodes and air microchannel for high sensitivity. Electromechanical interference between adjacent sensor units is addressed and eliminated through design optimization and the use of compliant electrical shield layers. A 1D sensor array is applied to gripping tasks, where the independent sensor readings enable detecting local and partial slipping. This study illustrates how the device can be used to explore the dynamics of local forces during the stages of pick up, slip, and release.

The SQUID Controller Unit for the LiteBIRD Space Mission: Description, Functional Tests and Early Performance Assessment

AbstractLiteBIRD is a satellite mission to be launched by JAXA in the early 2030s. It will measure the Cosmic Microwave Background (CMB) primordial B-modes with an unprecedented sensitivity. Microwave radiation will be detected by Transition Edge Sensors (TESs) arrays multiplexed in frequency domain and read by Superconducting QUantum Interference Devices (SQUIDs). The LiteBIRD SQUID Controller Unit (SCU), based on the heritage of the successful design used for the ground-based SPT3G experiment, presents some novel elements that make it suitable for a space-borne application. We compare our first breadboard model with the ground-based, Off-The-Shelf Components (COTS) version, by driving the same SQUID Array Amplifier (SAA) at 4 K, measuring relevant quantities such as noise, gain and bandwidth. We demonstrate that the noise added by our first prototype (including a switching part for redundancy purposes) never exceeds the noise added by the COTS-based electronics board, representing our benchmark. We also present the first noise estimates with the SAA cooled below 1 K, going closer to the conditions expected for LiteBIRD operation.

Dual Sensing Signal Decoupling Based on Thermoelectric Polymer Aerogels for Precise Temperature and Pressure Recognition

AbstractThe capability to emulate skin‐like temperature and pressure sensing is fundamental for next‐generation artificial intelligence products. However, detecting temperature and pressure simultaneously with a single sensor without signal interference is challenging. Herein, a novel PCC aerogel sensor composed of PEDOT:PSS, CNTs, and CNF via directional freezing technology is developed. The PCC sensor can decouple temperature and pressure stimuli into individual voltage and resistance signals. It exhibits high‐precision temperature sensing capabilities, boasting an exceptionally high Seebeck coefficient of 30.4 µV K‐1 and the ability to detect temperature variations as low as 0.1 K. PCC sensors show excellent sensitivity and fast response times for detecting static and dynamic pressures, as well as high stability after 1000 cycles. Its maximum pressure sensitivity can reach 159.1% kPa−1, and the lowest detection limit is 10 Pa. Additionally, its excellent thermoelectric properties also enable to generating thermopower from human skin for self‐powered pressure sensing. A 3×3 PCC sensor array has been proposed to simulate the unique features of human skin in temperature and pressure recognition. This work provides a scalable manufacturing strategy for multi‐functional tactile sensors.

All-Printed Finger-Inspired Tactile Sensor Array for Microscale Texture Detection and 3D Reconstruction.

Electronic skins are expected to replicate a human-like tactile sense, which significantly detects surface information, including geometry, material, and temperature. Although most texture features can be sensed in the horizontal direction, the lack of effective approaches for detecting vertical properties limits the development of artificial skin based on tactile sensors. In this study, an all-printed finger-inspired tactile sensor array is developed to realize the 3D detection and reconstruction of microscale structures. A beam structure with a suspended multilayer membrane is proposed, and a tactile sensor array of 12 units arranged in a dual-column layout is developed. This architecture enables the tactile sensor array to obtain comprehensive geometric information of micro-textures, including 3D morphology and clearance characteristics, and optimizes the 3D reconstruction patterns by self-calibration. Moreover, an innovative screen-printing technology incorporating multilayer printing and sacrificial-layer techniques is adopted to print the entire device. In additon, a Braille recognition system utilizing this tactile sensor array is developed to interpret Shakespeare's quotes printed in Grade 2 Braille. The abovementioned demonstrations reveal an attractive future vision for endowing bioinspired robots with the unique capability of touching and feeling the microscale real world and reconstructing it in the cyber world.

A bimetallic peroxidase-mimicking nanozyme with antifouling property for construction of sensor array to identify phosphoproteins and diagnose cancers

Protein phosphorylation is a significant post-translational modification that plays a decisive role in the occurrence and development of diseases. However, the rapid and accurate identification of phosphoproteins remains challenging. Herein, a high-throughput sensor array has been constructed based on a magnetic bimetallic nanozyme (Fe3O4@ZNP@UiO-66) for the identification and discrimination of phosphoproteins. Attributing to the formation of Fe-Zr bimetallic dual active centers, the as-prepared Fe3O4@ZNP@UiO-66 exhibits enhanced peroxidase-mimicking catalytic activity, which promotes the electron transfer from Zr center to Fe(II)/Fe(III). The catalytic activity of Fe3O4@ZNP@UiO-66 can be selectively inhibited by phosphoproteins due to the strong interaction between phosphate groups and Zr centers, as well as the ultra-robust antifouling capability of zwitterionic dopamine nanoparticles (ZNP). Considering the diverse binding affinities between various proteins with the nanozyme, the catalytic activity of Fe3O4@ZNP@UiO-66 can be changed to various degree, leading to the different absorption responses at 420 nm in the hydrogen peroxide (H2O2) - 2, 2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) system. By simply extracting different absorbance intensities at various time points, a sensor array based on reaction kinetics for the discrimination of phosphoproteins from other proteins is constructed through linear discriminant analysis (LDA). Besides, the quantitative determination of phosphoproteins and identification of protein mixtures have been realized. Further, based on the differential level of phosphoproteins in cells, the differentiation of cancer cells from normal cells can also be implemented by utilizing the proposed sensor array, showing great potential in disease diagnosis.

Functionalized carbon quantum dots fluorescent sensor array assisted by a machine learning algorithm for rapid foodborne pathogens identification

Food safety is a global concern, and conventional methods for detecting foodborne bacteria, such as polymerase chain reaction and enzyme-linked immunosorbent assay, often involve time-consuming processes, specialized equipment, or specific recognition of particular bacterial strains. There is an urgent need for more efficient and convenient detection methods for foodborne pathogens. This study addresses this need by introducing an easily constructed fluorescent sensor array for the identification of various foodborne bacteria. The sensor array comprises carbon quantum dots (CQDs) functionalized with ampicillin, polymyxin, and gentamicin, each exhibiting different affinities for binding with specific bacteria. Leveraging machine learning algorithms, the proposed sensor array enables rapid, accurate, and highly sensitive identification of foodborne pathogens. This approach offers a convenient solution for the development of rapid, accurate, and hypersensitive detection methods for multiple bioactive samples. In summary, the fluorescent sensor array in this study holds promise for advancing the field of foodborne bacteria detection.

A feature extractor for temporal data of electronic nose based on parallel long short-term memory network in flavor discrimination of Chinese vinegars

Volatile flavor is a key indicator of food quality which can directly affect consumer preference and purchase intention. Electronic nose is considered as a promising intelligent sensory analysis tool for food flavor assessment, however, extracting effective features from the gas sensor array is still a major challenge, which largely determines the performance of subsequent classifiers. Here, a parallel long short-term memory (LSTM) network is proposed as a feature extractor for automatically extracting features from the whole time series of sensor responses in flavor discrimination of five Chinese vinegars. The network was trained by the temporal data from the sensor array and yielded different feature patterns corresponding to different vinegars, which were then fed to other conventional classifiers for pattern recognition. We also evaluated the influence of the extracted feature dimension that is related to the dimension of the hidden state of the LSTM layer on the classification performance. The results indicate that a larger dimension of extracted feature is unnecessary for promoting classification accuracy, instead, the optimum dimension 4 of the hidden state gives the highest accuracy of 95.8% in this application under the softmax evaluator. Moreover, much higher accuracies were obtained when combined with other sophisticated classifiers such as support vector machine. The results demonstrate that the proposed network is competent to extract features directly and automatically from the temporal data of the electronic nose.

InGaN/GaN multi-quantum well nanowires: Enhanced trace-level NO2 detection for environmental and breath analysis

The World Health Organization highlights nitrogen dioxide (NO2) as a key atmospheric pollutant impacting air quality and human health. To date, several sensors have been proposed for NO2 detection and disease monitoring, but their effectiveness is limited because of low sensitivity and high operational temperatures. Herein, we propose a selective and sensitive NO2 sensor based on indium gallium nitride/gallium nitride multiple quantum wells (InGaN/GaN-MQWs). We explored different InGaN/GaN-MQWs configurations (InGaN/GaN-MQW1, InGaN/GaN-MQW2, InGaN/GaN-MQW3), developed through metal–organic chemical vapor deposition (MOCVD), to analyze their impact on gas sensing performance. Our findings reveal that the InGaN/GaN-MQW2 sensor at 150 °C exhibits a response 4.0, 3.1, and 1.65 times greater than GaN-NWs, InGaN/GaN-MQW1, and InGaN/GaN-MQW3 sensors, respectively. The limit of detection (LOD) for the InGaN/GaN-MQW2 sensor is 3 parts per billion (ppb), significantly lower than the NO2 threshold value of 53 ppb. Under ultraviolet (UV) light, the response of InGaN/GaN-MQW2 is 5.51-fold, 4.18-fold, and 1.82-fold higher than that of the GaN-NWs, InGaN/GaN-MQW1, and InGaN/GaN-MQW2 sensors, respectively. Importantly, our hybrid nanocomposite-based electronic nose (e-nose) sensor arrays could distinguish between healthy and simulated unhealthy breaths with high accuracy, promising a new era of efficient, non-invasive disease detection through breath analysis.

Development of an Integrated CMUTs-based Resonant Biosensor for Label-Free Detection of DNA with Improved Selectivity by Ethylene-Glycol Alkanethiols

Gravimetric resonant-inspired biosensors have attracted increasing attention in industrial and point-of-care applications, enabling label-free detection of biomarkers such as DNA and antibodies. Capacitive micromachined ultrasonic transducers (CMUTs) are promising tools for developing miniaturized high-performance biosensing complementary metal oxide silicon (CMOS) platforms. However, their operability is limited by inefficient functionalization, aggregation, crosstalk in the buffer, and the requirement for an external high-voltage (HV) power supply. In this study, we aimed to propose a CMUTs-based resonant biosensor integrated with a CMOS front-end interface coupled with ethylene-glycol alkanethiols to detect single-stranded DNA oligonucleotides with large specificity. The topography of the functionalized surface was characterized by energy-dispersive X-ray microanalysis. Improved selectivity for on-chip hybridization was demonstrated by comparing complementary and non-complementary single-stranded DNA oligonucleotides using fluorescence imaging technology. The sensor array was further characterized using a five-element lumped equivalent model. The 4-mm2 application-specific integrated circuit chip was designed and developed through 0.18 μm HV Bipolar-CMOS-Double diffused MOS (DMOS) technology (BCD) to generate on-chip 20V HV boosting and to track feedback frequency under a standard 1.8 V supply, with a total power consumption of 3.8 mW in a continuous mode. The measured results indicated a detection sensitivity of 7.943 × 10−3 μmol∙ L−1∙Hz−1 over a concentration range of 1 to 100 μmol∙L−1. In conclusion, the label-free biosensing of DNA under dry conditions was successfully demonstrated using a microfabricated CMUT array with a 2 MHz frequency on CMOS electronics with an internal HV supplier. Moreover, ethylene-glycol alkanethiols successfully deposited self-assembled monolayers on aluminum electrodes, which has never been attempted thus far on CMUTs, to enhance the selectivity of bio-functionalization. The findings of this study indicate the possibility of full-on-chip DNA biosensing with CMUTs.

Measurement of Somatosensory Evoked Magnetic Fields Using an Adjustable Magnetoresistive Sensor Array

An adjustable helmet-style magnetoresistive (MR) sensor array with room-temperature magnetic flux sensors was developed to demonstrate the simultaneous multipoint measurement of the somatosensory evoked magnetic field (SEF). Utilizing the extended sensor length, we designed the array to permit individual radial adjustment of each sensor, thereby achieving a precise fit to the varied head geometries of different subjects. Furthermore, the geometry of the sensor array precisely adjusted for the individual subject was quickly obtained by calibration. The SEF was measured in three healthy subjects using an array of 30-channel MR sensors placed on the left hemisphere of the head with median nerve stimulation in the right wrist and averaged over 8,000 measurements. An M20 component considered to originate from the primary somatosensory cortex was observed at an approximate latency of 20 ms of the magnetic field waveform in all cases (maximum amplitude of 725 ± 257 fT, peak latency of 20.5 ± 0.45 ms). The phase inversion observed around C3 in the international 10-20 system corresponded to the palmar area of the primary somatosensory cortex on the contour map of the magnetic field at the M20 peak. The MR sensor, an affordable and easy-to-use magnetic sensor that does not require a zero-field environment nor a cryogenic apparatus, was successfully used for simultaneous multipoint SEF measurements in humans and provides a promising system for realizing magnetoencephalography application devices.

A battery-free, wireless, flexible bandlike e-nose based on MEMS gas sensors for precisely volatile organic compounds detection

Real-time detection of VOCs in the car cabin is crucial for protecting driver’s health. MOS gas sensors, with the advantages of low cost, simplicity and fast response, are widely used for gas monitoring. However, they typically exhibit broad-spectrum responsiveness to multiple gases. Despite numerous improvement strategies proposed from the perspective of MOS material modification, achieving ideal gas selectivity remains challenging. An e-nose based on gas sensor array is a promising approach for gas identification. However, the hardware circuit system and battery supply module in traditional e-nose result in large size, complex structure, and inconvenience in use. In this work, we synthesized MOS materials with different morphologies to form a microarray containing six MEMS gas sensors, and developed a novel belt-shaped wireless and battery-free flexible e-nose, where a mobile phone serves as its wireless power supply and data communication system. This design provides a flexible e-nose with excellent wearability and safety, allowing users to monitor surrounding gas conditions at any time conveniently. Subsequently, we design pattern recognition models using algorithms such as MLP, RF, XGBoost, and LGBM, in combination with ensemble learning strategies, achieving high accuracy in VOCs classification and low-error concentration prediction. This flexible e-nose holds significant application value for gas detection in car cabins and indoor environments.

Esters-targeted colorimetric sensor array for the authenticity discrimination of strong-aroma baijiu with different origins

Baijiu authenticity has been a frequent problem driven by economic interests in recent years, so it is important to discriminate against baijiu with different origins. Herein, we proposed a simple and efficient esters-targeted colorimetric sensor array mediated by hydroxylamine hydrochloride. Esters undergo a nucleophilic addition reaction with hydroxylamine hydrochloride to form hydroxamic acid, which rapidly forms a purplish red ferric hydroxamate under FeCl3·6H2O. Bromophenol blue and rhodamine B enrich the color effects. The array detected 12 esters with a detection limit on the order of 10−5 of most esters and 16 mixed esters with R2 > 0.999 and recoveries close to 100%. Otherwise, for discriminating 34 strong-aroma baijius (SABs), the array has an accuracy of 98% according to the origin, and 95% according to the grades, with a response time of 1 min. This study provides a new strategy for authenticity determination and quality control of baijiu.

A fluorescent sensor array based on antibiotic-stabilized metal nanoclusters for the multiplex detection of bacteria.

To address the need for facile, rapid detection of pathogens in water supplies, a fluorescent sensing array platform based on antibiotic-stabilized metal nanoclusters was developed for the multiplex detection of pathogens. Using five common antibiotics, eight different nanoclusters (NCs) were synthesized including ampicillin stabilized copper NCs, cefepime stabilized gold and copper NCs, kanamycin stabilized gold and copper NCs, lysozyme stabilized gold NCs, and vancomycin stabilized gold/silver and copper NCs. Based on the different interaction of each NC with the bacteria strains, unique patterns were generated. Various machine learning algorithms were employed for pattern discernment, among which the artificial neural networks proved to have the highest performance, with an accuracy of 100%. The developed prediction model performed well on an independent test dataset and on real samples gathered from drinking water, tap water and the Anzali Lagoon water, with prediction accuracy of 96.88% and 95.14%, respectively. This work demonstrates how generic antibiotics can be implemented for NC synthesis and used as recognition elements for pathogen detection. Furthermore, it displays how merging machine learning techniques can elevate sensitivity of analytical devices.

3D-Printed Soft Temperature Sensors Based on Thermoelectric Effects for Fast Mapping of Localized Temperature Distributions.

We propose a novel design of thermoelectric (TE) effect-based soft temperature sensors for directly monitoring localized subtle temperature stimuli. This design integrates rheology-engineered three-dimensional (3D) printing of high-performance carbon-based TE materials and polymer-based viscoelastic materials with low thermal conductivity. Rheological engineering of carbon nanotube (CNT) TE inks ensures the 3D printing of highly sensitive TE sensing units on directly written 3D soft platforms. Additionally, we pre-dope CNT inks with p- and n-type organic dopants to achieve high sensitivity and a fast response to temperature changes. The introduced 3D soft platforms with low thermal conductivity lead to an efficient thermal gradient on TE sensing units in the out-of-plane direction. Furthermore, encapsulating the temperature sensor array with the same polymer-based materials as the 3D soft platforms facilitates independent detection of localized temperature stimuli by minimizing thermal interaction between sensing units, resulting in precise temperature mapping by localized detection. Our 3D-printed soft temperature sensors exhibit high sensitivity to relatively small temperature changes, with a minimum sensing resolution of 0.1 K within tens of milliseconds. Moreover, the temperature sensor array not only detects localized temperature stimuli by imaging the temperature distribution but also demonstrates remarkable mechanical reliability against repetitive deformation with high accuracy.

Unraveling the structure-chirality sensing relationship between achiral anthracene-based tetracationic nanotubes and nucleosides in aqueous host-guest complexation

In biological systems, nucleosides play crucial roles in various physiological processes. In this study, we designed and synthesized four achiral anthracene-based tetracationic nanotubes (1–4) as artificial hosts and chiroptical sensors for nucleosides in aqueous media. Notably, different nanotubes exhibit varied chirality sensing on circular dichroism (CD)/circularly polarized luminescence (CPL) spectra through the host-guest complexation, which prompted us to explore the factors influencing their chiroptical responses. Through systematic host-guest experiments, the structure-chirality sensing relationship between achiral anthracene-based tetracationic nanotubes and nucleosides in the host-guest complexation was unraveled. Firstly, the CD response originates from the anthracene rings situated at the side-wall position, resulting from the right-handed (P)- or left-handed (M)-twisted conformation of the macrocyclic structure. Secondly, the CPL signal is influenced by the presence of anthracene rings at the linking-wall position, which results from intermolecular chiral twisted stacking between these anthracene rings. Therefore, these nanotubes can serve as chiroptical sensor arrays to enhance the accuracy of nucleotide recognition through principal component analysis (PCA) analysis based on the diversified CD spectra. This study provides insights for the construction of adaptive chirality from achiral nanotubes with dynamic conformational nature and might facilitate further design of chiral functional materials for several applications.

An Improved Multi-frequency Eddy Current Array Sensor for Detecting Micro-Defects of Welding Seam in Metal Plates

This study proposes an improved multi-frequency eddy current array sensor for the accurate detection of micro-defects (under-welding, over-welding, partial welding breakage, and minute penetration holes) of welding seam in metal plates. The primary goal is to further reduce the external and internal noise of the induced coil by optimizing the composition structure of the eddy current probe, thereby improving its sensitivity and signal-to-noise ratio in micro-defect detection. In response to the issue of external noise, this research introduces a gradient differential coil model, which can compensate for initial errors based on a geometrically balanced structure. By establishing a 3D simulation model and a physical mechanical structure, the suppression effect of this structure on excitation magnetic field and external environmental interference is verified. Furthermore, for the internal noise, a mathematical model of the direct stranded litz wire is established. Theoretical calculations and experimental validation confirm that the litz wire exhibits lower eddy current loss and higher Q value compared to traditional copper conductors, thus mitigating internal noise and improving signal-to-noise ratio. On this basis, the excitation mode of the eddy current coil probe array is set to a multi-frequency excitation method based on a center frequency, greatly reducing the electromagnetic interference between channels. Experimental results demonstrate that the improved eddy current sensor can effectively recognize as low as Φ1 mm weld penetration defect at a lift-off value of 0.5mm, achieving a remarkably high signal-to-noise ratio of approximately 17.7dB.

High-performance gas sensor utilizing g-C3N4/In2O3 composite for low concentration prediction to NO2

The demand for gas sensors is experiencing rapid growth, driven by the increasing need for air quality detection in the face of environmental pollution and industrial emissions. However, the widely used metal oxide semiconductor (MOS) gas sensors suffer from drawbacks such as low ability to predict gas concentration and poor selectivity. In this work, a sensor array was fabricated consisting of 10 various gas sensors and combined with an advanced deep learning algorithm to achieve precise predictions of nitrogen dioxide (NO2) at low concentrations. First, graphitic carbon nitride (g-C3N4) and In2O3 nanoparticles were composited with different mass ratios, resulting in the formation of distinct g-C3N4/In2O3 composites as sensing materials. Afterward, SEM, TEM, and XRD were employed to characterize the morphology, structure, and elemental composition of the samples. Furthermore, sensing properties including response, selectivity, and repeatability, are studied for 10 gas sensors based on g-C3N4/In2O3 composites. Finally, the convolutional neural network-efficient channel attention-gate recursive unit (CNN-AGRU) model was proposed to analyze the patterns of the signals collected from the sensor array when exposed to various NO2 concentrations from 1 to 9 ppm. The results demonstrated the integration of high-selectivity sensing materials to NO2 and an advanced deep learning algorithm CNN-AGRU enables to realize a high concentration prediction accuracy of about 97.04% and a low prediction error of 0.20527 ppm for NO2 gas.

Surface heat fluxes at coarse blocky Murtèl rock glacier (Engadine, eastern Swiss Alps)

Abstract. We estimate the surface energy balance (SEB) of the Murtèl rock glacier, a seasonally snow-covered permafrost landform with a ventilated coarse blocky active layer (AL) located in the eastern Swiss Alps. We focus on the parameterisation of the turbulent heat fluxes. Seasonally contrasting atmospheric conditions occur in the Murtèl cirque, with downslope katabatic jets in winter and a strongly unstable atmosphere over the heated blocky surface in summer. We use a novel comprehensive sensor array both above the ground surface and in the coarse blocky AL to track the rapid coupling by convective heat and moisture fluxes between the atmosphere, the snow cover, and the AL for the time period September 2020–September 2022. The in situ sensor array includes a sonic anemometer for eddy-covariance flux above-ground and sub-surface long-wave radiation measurements in a natural cavity between the AL blocks. During the thaw seasons, the measurements suggest an efficient (∼ 90 %) export of the available net radiation by sensible and latent turbulent fluxes, thereby strongly limiting the heat available for melting ground ice. Turbulent export of heat and moisture drawn from the permeable AL contributes to the well-known insulating effect of the coarse blocky AL and partly explains the climate resiliency of rock glaciers. This self-cooling capacity is counteracted by an early snow melt-out date, exposing the low-albedo blocky surface to the intense June–July insolation and causing reduced evaporative cooling due to exacerbated moisture scarcity in the near-surface AL during dry spells. With climate change, earlier snowmelt and increased frequency, duration, and intensity of heat waves and droughts are projected. Regarding the parameterisation of the turbulent fluxes, we estimated the year-round turbulent fluxes using a modified Louis (1979) scheme. The monthly SEB is closed within 20 W m−2 except during the snowmelt months and under katabatic drainage winds in winter. Detected sensible turbulent fluxes from nocturnal ventilation processes, although a potentially important ground cooling mechanism, are within our 20 W m−2 uncertainty because nighttime wind speeds are low. Wintertime katabatic wind speeds needed to be scaled to close the SEB, which hints at the limits of parameterisations based on the Monin–Obukhov similarity theory in complex mountain terrain and katabatic drainage winds. The present work contributes to the process understanding of the SEB and climate sensitivity of coarse blocky landforms.

A Review on Sensor Technologies and Control Methods for Mobile Robot with Obstacle Detection System

A Review on Sensor Technologies and Control Methods for Mobile Robot with Obstacle Detection System

ANKLE JOINT MOTION RECOGNITION SYSTEM AND ALGORITHM OPTIMIZATION BASED ON PLANTAR PRESSURE

Due to the current focus of research on ankle rehabilitation robots on structural design, there is still limited research on ankle human–machine interaction technology. In order to enable rehabilitation robots to conduct personalized rehabilitation training based on patients’ ankle movement intentions, we propose a new ankle motion recognition method based on plantar pressure. First, we designed a plantar pressure collection system based on array sensors. Then, we collected nine types of ankle joint motion pressure data from five volunteers and conducted algorithm selection, data processing, and algorithm optimization. Finally, we proposed a small sample optimization algorithm based on support vector machine, with an average recognition rate of 93.16%. The recognition method proposed in this paper can be combined with ankle rehabilitation robots to achieve active rehabilitation functions, laying the foundation for the clinical application of active rehabilitation technology.