Search Results (249)

Parkinson’s disease (PD) is associated with gait impairment, bilateral asymmetry, and increased gait variability, highlighting the need for objective and interpretable wearable gait assessment. Plantar insole recordings directly capture foot–ground loading, but their use in PD assessment is often limited by global or low-order descriptors that do not fully represent regional loading organization. This study proposes a region-specific information-theoretic framework for PD gait assessment using wearable plantar-pressure insoles. Bilateral plantar insole signals were reorganized into five anatomical regions: heel, rearfoot, midfoot, forefoot, and toe. Self-information index (SII), Shannon entropy (EN), negentropy (NEG), sample entropy (SEN), and Kullback–Leibler divergence (KL) features were extracted to characterize self-information fluctuation, probabilistic uncertainty, non-Gaussian organization, temporal irregularity, and directional distributional discrepancy in plantar-pressure dynamics. The resulting feature representation was evaluated at gait-cycle, walking-recording, and subject-independent levels using conventional classifiers, ablation analysis, subject-balanced cycle aggregation, and an information-theoretic three-dimensional feature-space rule model (ITFS-RM). KNN achieved an accuracy of 0.9668 at the gait-cycle level, and MLP achieved an accuracy of 0.9344 at the walking-recording level. Under stricter subject-independent evaluation, the accuracy was 0.8475, and subject-balanced-cycle aggregation achieved an accuracy of 0.8655. Region-specific analysis and ablation experiments showed spatially heterogeneous HC–PD differences, with the toe region showing the most consistent contribution. SII, KL, and NEG provided stable discriminative contributions, particularly in toe-related and regional-transition features. ITFS-RM provided explicit feature combinations, value ranges, and spatial rule boundaries for interpretable walking-recording level and subject-grouped separation. These results support region-specific information-theoretic analysis as an interpretable representation of plantar-pressure dynamics for PD gait assessment and emphasize the need for subject-wise validation when repeated walking recordings are available. Full article

Background/Objectives: Adherence to partial weight-bearing prescriptions (PWBP) is challenging. The use of audio biofeedback (AB) can potentially help to better implement PWBP. This study aimed to investigate the effect of continuous AB provided by sensor insoles on partial weight-bearing load during functional tasks. Methods: Twenty older patients received a single AB training for PWBP management postoperatively. The prescribed limb load was measured (ground reaction force) during four activities (walking, walking with a 5 kg backpack, sitting–standing–sitting, and standing) with force–sensor insoles with continuous AB (n = 10) or without AB (n = 10). Individual deviation from the prescribed load and the influence of age and cognitive function were analyzed. Results: The intervention group (continuous AB) managed PWBP better for three out of four activities: The relative deviation was 104.4% ± 144.2 vs. 164.5% ± 164.5 for the 5 kg backpack walk, 57.6% ± 107.2 vs. 86.8% ± 122.4 for the sit–stand–sit task, and 8.0% ± 102.3 vs. 38.8% ± 131.7 for standing. For the 3 min walking activity, the relative deviation was 127.7% ± 121.9 vs. 116.0% ± 133.0 in favor of the control group. Mean differences were not statistically significant for any of the activities. Conclusions: After a single training session with continuous AB, PWBP can only be insufficiently met. Studies with multiple training sessions seem necessary to further test the potential of continuous AB in the management of PWBP. Full article

Wearable force-sensitive insoles enable quantitative gait analysis as a screening aid for neurodegenerative diseases (NDDs), yet prevailing machine learning pipelines give point predictions with no per-case reliability estimate, no intrinsic explanation, and no way to curate their own knowledge base. Case-Based Reasoning (CBR) mirrors clinical reasoning, but deployed healthcare CBR systems typically implement only partial R⁴ cycles, omitting Revise and Retain. We propose CogCBR, a sensor-driven framework that operationalizes the complete R⁴ cycle—Retrieve, Reuse, Revise, Retain—for gait-based NDD screening within Richter’s four knowledge containers, pairing weighted case retrieval with confidence-based clinical triage and a label-verified case-base maintenance policy. On the PhysioNet GaitPDB cohort, CogCBR attains an AUC of 0.861—statistically on par with the strongest tuned baseline under matched tuning, yet the only method evaluated that also provides confidence-based triage, case-based explanation, and longitudinal case-base maintenance, the last validated in a deployment-style streaming simulation. An independent-cohort evaluation on GaitNDD yields an AUC of 0.902; under a stricter cross-modality transfer, however, CogCBR does not exceed the strongest classical baseline, which is also reported. With sub-millisecond inference and a compact footprint, CogCBR suits resource-constrained wearable and edge-health platforms. Prospective longitudinal clinical evaluation and validation in pre-clinical cohorts are left as future work. Full article

Accurate plantar pressure distribution is important for biomechanics, gait analysis, rehabilitation, and diabetic foot assessment. However, wearable plantar pressure systems are often limited by sparse sensor layouts due to hardware complexity, power consumption, and user comfort constraints. This study proposes a multimodal deep learning framework for static plantar pressure prediction using plantar geometry information and sparse landmark constraints. A convolutional block attention module U-Net architecture was developed to integrate plantar geometry and sparse landmark modalities through dual-encoder feature fusion with attention refinement. Different network architectures, fusion strategies, and landmark densities were systematically evaluated using a controlled-variable experimental design. Results demonstrated that feature-level fusion consistently outperformed data-level fusion and unimodal configurations across all landmark densities. The proposed model achieved the best performance with a normalized root mean square error of 0.087 using 16 landmarks, and the same model maintained a normalized root mean square error of 0.138 using only two landmarks, indicating promising reconstruction performance even under highly sparse sensing conditions. Marginal contribution and synergy analyses further showed that feature-level fusion more effectively captured complementary interactions between plantar geometry and sparse anatomical guidance, particularly under sparse landmark conditions. These findings suggest that multimodal feature-level fusion provides an effective strategy for sparse-to-dense plantar pressure reconstruction and may support the development of low-cost intelligent insole systems for biomechanical monitoring and clinical applications. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Diabetic foot ulcers represent a severe complication of diabetes mellitus, affecting millions of adults worldwide and often leading to hospitalization and amputation. Diabetic neuropathy increases the risk of plantar injuries, while the lack of continuous monitoring and delayed detection contributes to the progression of these lesions. This study presents a low-cost smart insole system for continuous plantar pressure monitoring and screening of plantar pressure patterns associated with diabetic neuropathy. The system integrates piezoresistive sensors distributed across key regions of the foot, connected to a low-power ESP32 microcontroller for data acquisition. Measurements are transmitted via Bluetooth Low Energy to a mobile application that enables real-time visualization, user management, and storage in a MySQL database for historical data consultation. Data processing employs a convolutional neural network configured to classify plantar pressure patterns between non-diabetic individuals and diabetic patients presenting neuropathic alterations. System validation demonstrated 88% accuracy, 88% recall, and 87% F1-score in classifying plantar pressure patterns. The results confirm that the combination of low-cost hardware and open-source software constitutes a viable and scalable solution for screening biomechanical alterations associated with diabetic foot complications. Full article

Osteoarthritis (OA) is a highly prevalent musculoskeletal disorder and a major cause of disability, posing growing challenges for healthcare systems worldwide. Conventional supervised clinical assessments provide valuable insights but are largely limited to cross-sectional snapshots and often fail to reflect the variability of real-world functioning, physical activity patterns, and symptom fluctuations experienced by individuals with OA, especially those with knee OA. This perspective introduces a multisensor digital phenotyping framework for smart knee OA assessment, integrating supervised laboratory evaluations with unsupervised continuous monitoring in daily living environments using wearable sensors, smart insoles, activity trackers, and mobile devices. Feasibility was tested in 40 participants (20 knee OA patients, 20 controls). Raw data from questionnaires, electronic goniometry, dynamometry, force plate, connected insoles, and seven-day home monitoring were harmonized via a standardized pipeline aligned with the ICF framework. The pipeline employed anomaly detection, missing data imputation, z-score normalization, and cloud-based storage. This framework is envisioned to facilitate advanced data integration and machine-learning-ready analytics, enabling longitudinal monitoring, pattern recognition, and individualized health profiling. By conceptually bridging cross-sectional and continuous sensing modalities, this approach has the potential to enhance ecological validity, support earlier identification of functional decline, and inform data-driven clinical decision-making. Key methodological, technological, and ethical challenges—including data quality, interpretability, privacy, digital literacy, and clinical adoption—are also highlighted. Overall, this paper underscores the promise of AI-enabled multisensor digital phenotyping to advance smart, personalized, and precision healthcare for individuals with knee OA. Full article

Accurate detection of initial contact (IC) during side-cutting is essential for evaluating m. semitendinosus (ST) pre-activity, a protective mechanism against ACL injury in team sport athletes. This study developed two insole pressure sensor (IPS) algorithms—a body weight-based and a criteria-based algorithm—for IC detection and evaluated their agreement with force-plate-derived IC based on vertical ground reaction forces (vGRF). Twenty-six adult female athletes performed sport-specific side-cutting while IPS, vGRF, and ST electromyography were recorded. IPS-derived IC events were compared with vGRF-derived IC, and ST pre-activity within 50 ms prior to IC was compared between methods. Agreement and limits of agreement (LoA) were evaluated using Bland–Altman analysis. The body weight-based IPS algorithm showed a systematic delay in IC detection of 9.2 ms (LoA: 4.1 to 14.3 ms) and a −3.5 percentage point bias in ST pre-activity (LoA: −8.9 to 1.9% of MVC). In contrast, the criteria-based IPS algorithm, demonstrated minimal bias in IC detection (−0.1 ms; LoA: −3.5 to 3.4 ms) and ST pre-activity (−0.1% MVC; LoA: −1.9 to 1.7% of MVC). These findings suggest the criteria-based IPS algorithm enables accurate IC detection, supporting its potential for practical monitoring of ST pre-activity during sports-specific side-cutting outside laboratory environments. Full article

Objectives: To systematically synthesize existing evidence on functional biomechanical tests of the foot and ankle in physiotherapy and sports, focusing on their outcome measures, compatibility with wearable sensor technologies, and psychometric properties. Methods: We performed a systematic review (PRISMA-guided) of PubMed, Web of Science, PEDro, and SPORTDiscus from inception to December 2025. Eligible studies evaluated functional foot/ankle biomechanics in athletes, healthy adults, or adults with musculoskeletal foot/ankle conditions using wearable sensors (e.g., IMUs, wireless pressure insoles). Two reviewers independently screened, extracted data, and appraised methodological quality using the COSMIN Risk of Bias tool, applying property-specific ratings. Heterogeneity precluded meta-analysis; findings were narratively synthesized and tabulated. Results: Twenty full texts were reviewed; four studies (n = 83 participants) met the inclusion criteria. Wearable devices included foot- or trunk-mounted IMUs and wireless pressure insoles. Reported outcomes spanned temporal gait events and inner-stance phases, vertical ground reaction force (vGRF) and centre-of-pressure trajectories, running step rate/stride length, and jump counts in competition. Validity was most frequently assessed: foot-worn IMUs showed millisecond-level agreement with in-shoe pressure references for stance and inner-stance events; pressure insoles demonstrated acceptable agreement with force plates for vGRF/COP alongside fair-to-excellent test–retest reliability; foot- vs. shank-mounted IMUs provided strong agreement for running step rate and stride length; and competition-based jump detection using IMUs achieved high sensitivity. Across studies, reliability indices were inconsistently reported, measurement error (SEM/MDC) was sparse, and MCID was not reported. The COSMIN appraisal ranged from very good/adequate to inadequate, driven primarily by small sample sizes, non-gold-standard comparators, and incomplete psychometric reporting. Full article

(1) Background: Gait analysis technologies have advanced; however, traditional systems like optical motion capture are lab-bound and costly, limiting rehabilitation monitoring. This cross-sectional study evaluates self-powered triboelectric nanogenerator (TENG) insoles combined with IMU sensors to assess gait asymmetry, plantar pressure signatures, age effects and injury history in rehabilitation patients, aiming to enable portable, battery-free phenotyping. (2) Methods: Fifty-three patients (22 females, 31 males; age, 29 ± 26 years) from Astana clinics with trauma histories (e.g., spine, ankle, fractures) and 10 healthy references underwent a 2 min walk test (2MWT). TENG insoles captured plantar loading; ankle/knee IMUs measured spatiotemporal parameters (cadence, asymmetry). The data were normalized; the analyses used an ANOVA and correlations (Python 3.14.3). (3) Results: The TENG sensors showed force/frequency linearity (up to 10 V at 20 N). The cadence averaged 101 ± 10 steps/min, declining with age (r = −0.31, p = 0.03) and fractures (r = −0.23, p = 0.04). The asymmetry varied (−54% to +31%) without category differences. Flatfoot (55%) was linked to lateral loading shifts; condition-specific waveform signatures emerged (e.g., lateral heel in ankle issues). (4) TENG-IMU systems feasibly capture gait phenotypes in heterogeneous cohorts, supporting out-of-lab monitoring for personalized rehabilitation without batteries. Prospective validation is required for further practical implications. Full article

The foot arch functions as a dynamic biomechanical system, maintained by the integrated actions of bones, ligaments, and muscles. A large body of clinical evidence indicates that, in addition to congenital foot deformities, acquired variations in the foot arch caused by factors such as poor gait, aging, weight, or injury can significantly affect quality of life. Early intervention upon detection of foot arch changes can help mitigate progression and prevent further deterioration. Despite the availability of multimodal sensor-integrated running platforms for gait analysis, such systems are inherently bulky and not conducive to routine walking measurement. To overcome the above limitations, this study employed a flexible plantar pressure insole with an integrated accelerometer and a dedicated acquisition circuit to capture plantar pressure and acceleration data. This smart insole system acquires plantar data, performs feature extraction via time–domain and wavelet analysis, and then employs machine learning to classify the foot arch type as a normal foot, flatfoot, or high-arched. A Random Forest classifier was then established to categorize foot arch types based on the collected data, which integrates numerous decision trees through bootstrap aggregation and random feature selection, with final classification determined by majority voting. A total of 30 volunteers participated, including 11 with normal arches, 11 with flat feet, and 8 with high arches. Compared with support vector machine, K nearest neighbors, and decision tree, the Random Forest achieved the highest recognition accuracy of 92%. This system reveals the patterns of plantar pressure distribution and acceleration fluctuations during walking across three foot arches and demonstrates that wavelet entropy can effectively quantify the changes in signal complexity included in foot arch differences. Compared with laboratory force plates, this system features lower cost and a smaller form factor, making it suitable for real-time monitoring. This system can lay the technical foundation for personalized foot orthopedics and health monitoring. Full article

Gait impairment is a common motor manifestation of Parkinson’s disease (PD), which is also frequently accompanied by other motor abnormalities such as bradykinesia, rigidity, postural instability, and movement asymmetry. These motor impairments are closely associated with reduced mobility and increased fall risk. Although wearable plantar insole sensing provides a promising basis for objective gait assessment, existing studies have mainly focused on conventional time- or frequency-domain descriptors, whereas the nonlinear complexity of gait, laterality-related imbalance, and deviation from normal gait patterns remain insufficiently characterized in an integrated manner. To address this gap, this paper proposes FID-Gait, which is a three-domain fusion framework for PD identification using instrumented insole data. The framework combines automated gait-cycle segmentation with multidomain feature modeling, including a fractal domain for nonlinear gait complexity, a plantar-loading–phase imbalance (PLPI) domain for loading asymmetry and temporal disturbance, and a covariance-adjusted deviation (CAD) domain for measuring deviation from normal gait patterns. Experiments on the PhysioNet Gait in Parkinson’s Disease dataset showed that FID-Gait achieved strong discriminative performance under multiple evaluation protocols. At the gait-cycle level, the selected MLP classifier achieved an accuracy of 99.11% and an F1-score of 99.47%. At the subject level, the selected AdaBoost classifier achieved the highest accuracy of 90.22% and the best F1-score reached 93.02%. Five-fold cross-validation further supported the robustness of the proposed representation, and leave-one-subject-out evaluation provided preliminary evidence of subject-independent generalization. Overall, FID-Gait provides an effective and interpretable framework for PD gait characterization and identification in offline experimental settings. Full article

Smart insoles equipped with pressure sensor matrices are increasingly used for gait analysis, yet high-density arrays compromise battery life and data throughput. This study aims to identify the optimal sparse sensor layout required to accurately estimate the Center of Pressure (CoP) by analyzing the trade-off between sensor number, spatial placement, and reconstruction error. Plantar pressure data were collected from twelve healthy participants walking at a self-selected speed using 16-sensor connected insoles. A combinatorial algorithm evaluated all $2^{16}-1$ possible sensor combinations to minimize the Root Mean Square Error (RMSE) in the antero-posterior, medio-lateral, and global Euclidean directions. Results reveal a non-linear convergence of accuracy that depends on the spatial axis. For longitudinal and global progression, a clear inflection point achieving sub-centimetric accuracy (RMSE < 5 mm) is reached at seven sensors. In contrast, medio-lateral tracking shows its largest discrete error reduction at five sensors, followed by gradual improvements at higher densities. Anatomical frequency analysis highlights distinct spatial requirements: the posterior heel is consistently selected for medio-lateral accuracy, while the lateral arch and metatarsal regions are critical for longitudinal progression. These findings suggest that while a minimum of seven strategically placed sensors enables robust CoP tracking across all spatial axes, optimal hardware design should remain task-specific. This work provides a data-driven framework for the development of energy-efficient wearable gait monitoring systems. Full article

Introduction: Although gait alterations associated with excess body weight have been widely studied, most available evidence comes from laboratory-based analyses, which limit ecological validity and the translation of findings into clinical practice. This study addresses this gap by examining gait biomechanics across BMI categories using portable sensor-based insoles that allow gait assessment in real-world conditions. Methods: A cross-sectional study including 96 adults categorized as normal weight (NW), overweight (OW), or obese (OB) was conducted. Gait biomechanics were recorded using PODOSmart^® intelligent insoles, which capture spatiotemporal and angular parameters during natural walking. Foot health, quality of life and comorbildities were evaluated throught valeted questionnarires. Differences between groups were analyzed using ANOVA and chi-square tests. Age and sex, known to influence gait, were comparable across BMI groups and were considered in the interpretation of the results. Results: Overall, the participants in the OB group exhibited reduced stride length, gait speed, and swing time, increased double-support time, and greater pronation–supination and progression angles than OW and NW participants. Partial eta-squared values (η²p) were predominantly medium to large, reinforcing the robustness of these between-group differences (e.g., double-support time, p > 0.001; η²p = 0.19). Individuals with obesity reported poorer general and foot health and more difficulty finding suitable footwear. BMI was also significantly associated with hypertension, dyslipidemia, arthritis, and depression (all p <0.05), whereas diabetes, cardiopathies, knee pain, and fatigue andwalking or social activity limitations showed no significant differences. Conclusions: By using portable gait analysis technology in ecological conditions, this study provides novel evidence of clinically meaningful gait impairments across BMI groups. Higher BMI is associated with clinically relevant gait impairments, poorer perceptions of foot and general health, and a higher prevalence of several comorbidities. Full article

Flexible piezoresistive sensors are often vulnerable to modal ambiguity and bending-induced drift, both of which can obscure true pressure and strain signals under practical operation. Here, we address these limitations by suppressing bending sensitivity at the device level and disambiguating tactile modes at the algorithmic level. We propose and fabricate a Ti₃C₂T_x/graphene oxide (GO) sandwich sensor in which the conductive network is positioned near the neutral axis, thereby ensuring that bending induces negligible axial strain in the active layer. In contrast, out-of-plane pressing enlarges microcontacts, while in-plane stretching disrupts percolation pathways. We develop a composite-beam model to quantify neutral-axis alignment and the resultant bending immunity, realize the device via a straightforward casting process, and systematically characterize its electromechanical response under bending, pressing, nail pressing, and stretching. To further reduce modal ambiguity and improve tactile recognition, a lightweight one-dimensional convolutional neural network (1D-CNN) was introduced to classify temporal resistance signals from the sensor. Experimental results showed that the 1D-CNN achieved a high classification accuracy of 98.52% under flat-state training and testing conditions, and maintained 96.67% accuracy when evaluated on bending-state samples, demonstrating strong robustness against bending-induced interference. Together, the neutral-axis device architecture and the learning-based inference pipeline deliver high sensitivity to pressing and stretching while markedly suppressing the response to bending, thereby enabling wrist-worn pulse monitoring, soft-robotic joint sensing, and plantar pressure insoles. Full article