Search Results (145)

Diabetic foot ulcers (DFUs) represent a critical global health issue, necessitating the development of advanced smart, flexible, and wearable sensors for continuous monitoring that are reimbursable within foot orthotics. This study presents the design and characterization of a pressure sensor implemented into a shoe insole to monitor diabetic wound pressures, emphasizing the need for a high sensitivity, durability under cyclic mechanical loading, and a rapid response time. This investigation focuses on the electrical and mechanical properties of carbon nanotube (CNT) composites utilizing Ecoflex and polydimethylsiloxane (PDMS). Morphological characterization was conducted using Transmission Electron Microscopy (TEM), Laser Confocal Microscopy, and Scanning Electron Microscopy (SEM). The electrical and mechanical properties of the CNT/Ecoflex- and the CNT/PDMS-based sensor composites were then investigated. CNT/Ecoflex was then further evaluated due to its lower variability performance between cycles at the same pressure, as well as its consistently higher capacitance values across all trials in comparison to CNT/PDMS. The CNT/Ecoflex composite sensor showed a high sensitivity (2.38 to 3.40 kPa⁻¹) over a pressure sensing range of 0 to 68.95 kPa. The sensor’s stability was further assessed under applied pressures simulating human weight. A custom insole prototype, incorporating 12 CNT/Ecoflex elastomeric matrix-based sensors (as an example) distributed across the metatarsal heads, midfoot, and heel regions, was developed and characterized. Capacitance measurements, ranging from 0.25 pF to 60 pF, were obtained across N = 3 feasibility trials, demonstrating the sensor’s response to varying pressure conditions linked to different body weights. These results highlight the potential of this flexible insole prototype for precise and real-time plantar surface monitoring, offering an approachable avenue for a challenging diabetic orthotics application. Full article

Foot plantar pressure refers to the pressure or force that the foot generates in contact with the ground, varying across different regions of the foot. This parameter is essential in static and dynamic analyses to access accurate diagnoses, study the human body biomechanics, create functional footwear designs, aid in rehabilitation and physiotherapy, and prevent injuries in athletes during sports practice. This study presents an experimental comparison between two different plantar pressure measurement devices, Pedar^® (sensorized insoles) and Physiosensing^® (pressure platform). The devices were selected based on their capacity to measure contact area and peak pressure points. Results showed that Physiosensing^® provided a more uniform measurement of the contact area, proving its efficiency for weight distribution and stability analysis applications, particularly in posture assessment and balance studies. The Pedar^® system showed higher capacity in peak pressure point detection. Therefore, the insole system is more suitable for applications requiring precise high-pressure zone localization. Comparative analysis highlights the strengths and limitations of each device and offers insights regarding its optimal usage in clinical, sports, and research settings. Full article

Plantar pressure monitoring is decisive in injury prevention, especially in at-risk populations such as people with diabetic foot. In this context, innovative solutions such as pneumatic insoles can be essential in plantar pressure management. This study describes the development of a variable pressure system that promotes the monitoring, stabilization, and offloading of plantar pressure through a pneumatic insole. This research was also intended to evaluate its ability to redistribute plantar pressure, reduce peak pressure in both static and dynamic conditions, and validate its pressure measurements by comparing the results with those obtained from a pedar^® insole. Tests were carried out under both static and dynamic conditions, before and after the pressure stabilization process by air cells and the subsequent pressure offloading. During the validation process, methods were used to evaluate the agreement between measurements obtained by the two systems. The results of the static test showed that pressure stabilization reduced pressure on the heel by 32.43%, distributing it to the metatarsals and toes. After heel pressure offloading, the reduction reached 42.72%. In the dynamic test, despite natural dispersion of the measurements, a trend to reduce the peak pressure in the heel, metatarsals, and toes was observed. Agreement analysis recorded 96.32% in the static test and 94.02% in the dynamic test. The pneumatic insole proved effective in redistributing and reducing plantar pressure, with more evident effects in the static test. Its agreement with the pedar^® system reinforces its reliability as a tool for measuring and managing plantar pressure, representing a promising solution for preventing plantar lesions. Full article

Parkinson’s disease (PD) is the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current clinical methods for the diagnosis and severity assessment of PD rely on the visual and physical examination of subjects and identifying key disease motor signs and symptoms such as bradykinesia, rigidity, tremor, and postural instability. In the present study, we developed a method for the diagnosis and severity assessment of PD using Gramian Angular Fields (GAFs) in combination with deep Convolutional Neural Networks (CNNs). Our model was applied to PD gait signals captured using pressure sensors embedded into insoles. Our results indicated an accuracy of 98.6%, a true positive rate (TPR) of 99.2%, and a true negative rate (TNR) of 98.5%, showcasing superior classification performance for PD diagnosis compared to the methods used in recent studies in the literature. The estimation of disease severity scores using gait signals showed a high accuracy for the Hoehn and Yahr score as well as the Timed Up and Go (TUG) test score (R² > 0.8), while we achieved a lower prediction performance for the Unified Parkinson’s Disease Rating Scale (UPDRS) and its motor component (UPDRSM) scores (R² < 0.2). These results were achieved using gait signals recorded in time windows as small as 10 s, which may pave the way for shorter, more accessible assessment tools for diagnosis and severity assessment of PD. Full article

This study aims to detect gait events using a smartphone combined with deep learning and evaluate the remote effects and clinical significance of this method in different elderly populations and patients with cerebral small vessel disease (CSVD). In total, 150 healthy individuals aged 20–70 years were asked to attach a smartphone to their thighs and walk six gait cycles at self-selected low, normal, and high speeds, using an insole pressure sensor as the reference standard for gait events. A deep learning model was then established using BiTCN-BiGRU-CrossAttention, and two models (TCN-GRU and BiTCN-BiGRU) were compared. In total, 48 elderly (25 healthy, 12 with mild cognitive impairment, 11 with Parkinson’s disease) participated in an online home assessment, completing single-task and cognitive dual-task walking. Overall, 35 CSVD patients participated in an offline clinical assessment, completing single-task, cognitive dual-task, and physical dual-task walking. The BiTCN-BiGRU-CrossAttention model had the lowest MAE for detecting gait events compared to the other models. All models had lower MAEs for detecting heel strikes than toe-offs, and the MAE for low and high walking was higher than for normal speed walking. There were significant differences (p < 0.05) in gait parameters (Cadence, Stride time, Stance phase, Swing phase, Stance time, Swing time, Stride length, and walking speed) between single-task and cognitive dual-task walking for all online elderly participants. CSVD patients showed significant differences (p < 0.05) in gait parameters (Cadence, Stride time, Stance phase, Swing phase, Stance time, Stride length, and walking speed) between single-task and cognitive dual-task and between single-task and physical dual-task walking. Full article

Post-op shoes (POSs) are commonly used after forefoot surgery to protect the surgical site. However, there are insufficient data on their impact on forefoot load during the rollover phase of walking. This study aims to analyze the effects of a commonly used POS on plantar pressures under the forefoot and to assess whether improper usage could affect pressure patterns. Sixteen healthy volunteers underwent three different walking tests on a straight tartan track. The test setting included walking barefoot, as well as normal walking and a modified heel-accentuated “limping” gait while wearing a common POS. The pressure distribution over the forefoot regions of interest was measured using sensor insoles and a pressure-measuring plate on the ground. Results show that only the heel-accentuated “limping” gait in the POS led to a significant reduction in pressure values over all anatomical regions compared to the normal barefoot gait. Furthermore, higher pressure values were found over the lesser toes during normal walking in the POS compared to normal barefoot walking. The findings highlight that the protective function of a POS relies on proper use, specifically the correct gait pattern. If used incorrectly, POS may even have unfavorable effects on the pressure on the operated forefoot and possibly even increase the risk of delayed healing or complications in comparison to barefoot walking. Therefore, strategies such as patient training in proper walking techniques should be incorporated into postoperative care. Full article

Ground reaction force (GRF) and center of pressure (COP) during walking are two important measures that could be used in a range of applications, from the control of devices such as exoskeletons to clinical assessments. Recording these measures requires fixed laboratory equipment such as force plates or expensive portable insoles. We present an alternative approach by developing a 3D-printed insole that uses pneumatic chambers and pressure sensors to estimate the net GRF and the anterior–posterior COP position. The intentionally simple design, using just two pneumatic chambers, can be fabricated using standard 3D printing technology and readily available soft materials. We used experimentally recorded data from a motion capture system along with parameter identification techniques to characterize and validate the insole while walking at different speeds. Our results showed that the insole was capable of withstanding repeated loading during walking—up to 1.2 times the body weight—and possessed a bandwidth high enough to capture gait dynamics. The identified models could estimate the GRF and the anterior–posterior COP position with less than 9% error. These results compare favourably with those of commercially available instrumented insoles and can be obtained at a fraction of their cost. This low-cost yet effective solution could assist in applications where it is important to record gait outside of laboratory conditions, but the cost of commercial solutions is prohibitive. Full article

With rapid advancements in lower-limb exoskeleton hardware, two key challenges persist: the accurate assessment of user biomechanics and the reliable control of device behavior in real-world settings. This study presents a modular, sensor-based system designed to enhance both biomechanical evaluation and control of lower-limb exoskeletons, leveraging advanced sensor technologies and fuzzy logic. The system addresses the limitations of traditional lab-bound, high-cost methods by integrating inertial measurement units, force-sensitive resistors, and load cells into instrumented crutches and 3D-printed insoles. These components work independently or in unison to capture critical biomechanical metrics, including the anteroposterior center of pressure and crutch ground reaction forces. Data are processed in real time by a central unit using fuzzy logic algorithms to estimate gait phases and support exoskeleton control. Validation experiments with three participants, benchmarked against motion capture and force plate systems, demonstrate the system’s ability to reliably detect gait phases and accurately measure biomechanical parameters. By offering an open-source, cost-effective design, this work contributes to the advancement of wearable robotics and promotes broader innovation and accessibility in exoskeleton research. Full article

This study introduces an integrated lower limb robotic orthosis with near-field electrospinning (NFES) piezoelectric sensors and a fuzzy logic-based gait phase detection system to enhance mobility assistance and rehabilitation. The exoskeleton incorporates embedded pressure sensors within the insoles to capture ground reaction forces (GRFs) in real-time. A fuzzy logic inference system processes these signals, classifying gait phases such as stance, initial contact, mid-stance, and pre-swing. The NFES technique enables the fabrication of highly oriented nanofibers, improving sensor sensitivity and reliability. The system employs a master–slave control framework. A Texas Instruments (TI) TMS320F28069 microcontroller (Texas Instruments, Dallas, TX, USA) processes gait data and transmits actuation commands to motors and harmonic drives at the hip and knee joints. The control strategy follows a three-loop methodology, ensuring stable operation. Experimental validation assesses the system’s accuracy under various conditions, including no-load and loaded scenarios. Results demonstrate that the exoskeleton accurately detects gait phases, achieving a maximum tracking error of 4.23% in an 8-s gait cycle under no-load conditions and 4.34% when tested with a 68 kg user. Faster motion cycles introduce a maximum error of 6.79% for a 3-s gait cycle, confirming the system’s adaptability to dynamic walking conditions. These findings highlight the effectiveness of the developed exoskeleton in interpreting human motion intentions, positioning it as a promising solution for wearable rehabilitation and mobility assistance. Full article

This work presents a comparative study of low cost and low invasiveness sensors (plantar pressure and inertial measurement units) for classifying cross-country skiing techniques. A dataset was created for symmetrical comparative analysis, with data collected from skiers using instrumented insoles that measured plantar pressure, foot angles, and acceleration. A deep learning model based on CNN and LSTM was trained on various sensor combinations, ranging from two specific pressure sensors to a full multisensory array per foot incorporating 4 pressure sensors and an inertial measurement unit with accelerometer, magnetometer, and gyroscope. Results demonstrate an encouraging performance with plantar pressure sensors and classification accuracy closer to inertial sensing. The proposed approach achieves a global average accuracy of 94% to 99% with a minimal sensor setup, highlighting its potential for low-cost and precise technique classification in cross-country skiing and future applications in sports performance analysis. Full article

Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in the fields of public fitness and sports. Musculoskeletal injuries, often resulting from unscientific training practices, are particularly prevalent, with the tibia being especially vulnerable to fatigue-related damage. Current tibial load monitoring methods rely mainly on laboratory equipment and wearable devices, but datasets combining both sources are limited due to experimental complexities and signal synchronization challenges. Moreover, wearable-based algorithms often fail to capture deep signal features, hindering early detection and prevention of tibial fatigue injuries. In this study, we simultaneously collected data from laboratory equipment and wearable insole sensors during in-place running by volunteers, creating a dataset named WearLab-Leg. Based on this dataset, we developed a machine learning model integrating Temporal Convolutional Network (TCN) and Transformer modules to estimate vertical ground reaction force (vGRF) and tibia bone force (TBF) using insole pressure signals. Our model’s architecture effectively combines the advantages of local deep feature extraction and global modeling, and further introduces the Weight-MSELoss function to improve peak prediction performance. As a result, the model achieved a normalized root mean square error (NRMSE) of 7.33% for vGRF prediction and 10.64% for TBF prediction. Our dataset and proposed model offer a convenient solution for biomechanical monitoring in athletes and patients, providing reliable data and technical support for early warnings of fatigue-induced injuries. Full article

The purpose of this study was to compare the mechanical properties of a non-cushioned minimalistic shoe and cushioned shoe during walking at 6 and running at 10 and 14 km∙h⁻¹ in habituated female runners. Twelve habituated female runners completed two trials (cushioned shoe vs. minimalist shoe) with three within-trial speeds (6, 10, and 14 km∙h⁻¹) in a counter-balanced design. Flexible pressure insole sensors were used to determine kinetic variables (peak vertical impact force, average loading rate, active vertical peak force, time to active peak vertical force, and impulse) and spatiotemporal variables (stride duration, cadence, ground contact time, swing time, and time to midstance). Cushioned running shoes exhibited greater energy absorption (690%), recovered energy (920%), and heat dissipation (350%). The cushioned shoes significantly reduced peak vertical impact (~12%) and average loading rate (~11%) at running speeds 10–14 km∙h⁻¹. However, these effects were not observed during walking, nor did the cushioned shoes influence peak active force, impulse, stride duration, ground contact or swing time. Cushioned running shoes provide significant benefits in energy absorption, energy recovery, and heat dissipation, which decrease impact-related forces and loading rates in female runners without changing the spatiotemporal variables of gait. Full article

Chronic ankle instability (CAI), due to its chronic nature and biomechanical complexity, is well-suited for continuous monitoring and tele-rehabilitation using wearable sensor technology. This study assessed whether a smart insole system, equipped with 4 force-sensing resistor sensors and an inertial measurement unit, combined with functional tests and biomechanical indices, could distinguish CAI patients from healthy controls. A total of 21 CAI patients (23.8 ± 5.1 years) and 16 controls (22.62 ± 2.60 years) completed a battery of functional performance tests while wearing the smart insole system. The results showed an increased medial-lateral pressure ratio in the CAI during heel raise (p = 0.031, effect size = 0.82) and hop tests, suggesting an everted foot position. Significant deviations in center-of-pressure trajectory during double-leg heel raises (p = 0.005, effect size = 1.10) suggested asymmetric motion coordination, while compensatory fluctuations of the lifted limb during single-leg balance tests (p = 0.011, effect size = 1.03) were greater in CAI patients. These findings facilitated the development of features to characterize CAI-specific movement patterns. Together, this system shows promise as a quantitative assessment tool for CAI, supporting improved treatment outcomes through tele-rehabilitation. Full article

Background/Objectives: Gait retraining is widely used in orthopedic rehabilitation to address abnormal movement patterns. However, retaining walking modifications can be challenging without guidance from physical therapists. Real-time auditory biofeedback can help patients learn and maintain gait alterations. This study piloted the feasibility of the musification of feedback to medialize the center of pressure (COP). Methods: To provide musical feedback, COP and plantar pressure were captured in real time at 100 Hz from a wireless 16-sensor pressure insole. Twenty healthy subjects (29 ± 5 years old, 75.9 ± 10.5 Kg, 1.73 ± 0.07 m) were recruited to walk using this system and were further analyzed via marker-based motion capture. A lowpass filter muffled a pre-selected music playlist when the real-time center of pressure exceeded a predetermined lateral threshold. The only instruction participants received was to adjust their walking to avoid the muffling of the music. Results: All participants significantly medialized their COP (−9.38% ± 4.37, range −2.3% to −19%), guided solely by musical feedback. Participants were still able to reproduce this new walking pattern when the musical feedback was removed. Importantly, no significant changes in cadence or walking speed were observed. The results from a survey showed that subjects enjoyed using the system and suggested that they would adopt such a system for rehabilitation. Conclusions: This study highlights the potential of musical feedback for orthopedic rehabilitation. In the future, a portable system will allow patients to train at home, while clinicians could track their progress remotely through cloud-enabled telemetric health data monitoring. Full article

Tibia shaft fractures are common lower extremity fractures that can require surgery and rehabilitation. However, patient recovery is often poor, partly due to clinicians’ inability to monitor bone loading, which is critical to stimulating healing. We envision a future of patient care that includes at-home monitoring of tibia loading using pressure-sensing insoles. However, one issue is missing portions of daily loading due to limited insole wear time (e.g., not wearing shoes all day). Here, we introduce a method for overcoming this issue with a wrist-worn fitness tracker that can be worn all day. We developed a model to estimate tibia loading from fitness tracker data and evaluated its accuracy during 10-h remote data collections (N = 8). We found that a fitness tracker, with trained and calibrated models, could effectively supplement insole-based estimates of bone loading. Fitness tracker-based estimates of loading stimulus—the minute-by-minute weighted impulse of tibia loading—showed a strong fit relative to insole-based estimates (R² = 0.74). However, insoles needed to be worn for a minimum amount of time for accurate estimates. We found daily loading stimulus errors less than 5% when insoles were worn at least 25% of the day. These findings suggest that a multi-sensor approach—where insoles are worn intermittently and a fitness tracker is worn continuously throughout the day—could be a viable strategy for long-term, remote monitoring of tibia loading in daily life. Full article