Among people with diabetes mellitus (DM), the two common strategies for decreasing peak plantar pressure (PPP) to reduce diabetic foot ulcers (DFUs) risks are to modify walking speeds and to change insole materials. This study reviewed the PPP reduction based on various walking speeds and insole materials. The articles were retrieved from four major scientific databases and manual search. We identified 1585 articles, of which 27 articles were selected for full-text analysis. We found that in faster walking speeds, the forefoot PPP was higher (308 kPa) than midfoot (150 kPa) and rearfoot (251 kPa) PPP. The appropriate walking speed for reducing the forefoot PPP was about 6 km/h for non-DM and 4 km/h for DM people. The forefoot PPP in DM people was 185% higher than that of non-DM people. Ethylene–vinyl acetate (EVA) insole material was the most popular material used by experts (26%) in the forefoot and reduced 37% of PPP. In conclusion, the suitable walking speed for DM was slower than for non-DM people, and EVA was the most common insole material used to decrease the PPP under the forefoot. The clinicians might recommend DM people to walk at 4 km/h and wear EVA insole material to minimize the DFUs.

The most frequent clinical complication is diabetes. Diabetes is characterized by elevated blood glucose levels resulting in sensory nerve damage or lesions. Diabetic foot wounds are often slow to heal and require medical attention and monitoring. This study evaluates the effect of far-infrared radiation on the microcirculation and plantar pressure in the diabetic foot. Ten diabetics and 4 nondiabetics were recruited in this study. The diabetic group was examined before and after the intervention in each month for 3 consecutive months. Four nondiabetic groups were also measured before and after the intervention for 2 weeks in each month. The surface temperature and blood flow in the diabetic foot was significantly improved (temperature: 32.1 ± 2.3°C vs 33.5 ± 2.2°C, P < .05; blood flow image: 118.3 ± 58.1 PU [perfusion unit] vs 50.4 ± 4.3 PU, P < .05). The sympathetic nerve activity index LF also increased from 40.8 ± 18.6% to 61.8 ± 13.5% (P = .07) in the second month. Plantar pressure tended to increase in the third month. This might indicate that far-infrared radiation could affect the mechanical properties of the plantar foot soft tissue. These results indicated that the effects of far-infrared radiation would improve blood circulation and change the soft tissue properties in the diabetic foot.

People with cerebral palsy (CP) suffer primarily from lower-limb impairments. These impairments contribute to the abnormal performance of functional activities and ambulation. Footprints, such as plantar pressure images, are usually used to assess functional performance in people with spastic CP. Detecting left and right feet based on footprints in people with CP is a challenge due to abnormal foot progression angle and abnormal footprint patterns. Identifying left and right foot profiles in people with CP is essential to provide information on the foot orthosis, walking problems, index gait patterns, and determination of the dominant limb. Deep learning with object detection can localize and classify the object more precisely on the abnormal foot progression angle and complex footprints associated with spastic CP. This study proposes a new object detection model to auto-determine left and right footprints. The footprint images successfully represented the left and right feet with high accuracy in object detection. YOLOv4 more successfully detected the left and right feet using footprint images compared to other object detection models. YOLOv4 reached over 99.00% in various metric performances. Furthermore, detection of the right foot (majority of people’s dominant leg) was more accurate than that of the left foot (majority of people’s non-dominant leg) in different object detection models.

Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.

Walking has been demonstrated to improve health in people with diabetes and peripheral arterial disease. However, continuous walking can produce repeated stress on the plantar foot and cause a high risk of foot ulcers. In addition, a higher walking intensity (i.e., including different speeds and durations) will increase the risk. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise. This study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. A wearable plantar pressure measurement system was used to measure plantar pressures during walking. An Artificial Neural Network (ANN) was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel (HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). Of the 12 participants, 10 participants (720 images) were randomly selected to train the classification model, and 2 participants (144 images) were utilized to evaluate the model performance. Experimental evaluation indicated that the ANN model effectively classified different walking speeds and durations based on the plantar region pressure images. Each plantar region pressure image (i.e., T1, M1, M2, and HL) generates different accuracies of the classification model. Higher performance was achieved when classifying walking speeds (0.8 m/s, 1.6 m/s, and 2.4 m/s) and 10 min walking duration in the T1 region, evidenced by an F1-score of 0.94. The dataset T1 could be an essential variable in machine learning to classify the walking intensity at different speeds and durations.

Background: Biomarkers play an important role in the diagnosis of early-stage Alzheimer disease (AD), with the hippocampal emerging as the most reliable indicator of AD pathology. Elucidation of the patient’s left and right hippocampal volumes warrants further consideration. Therefore, caution should be exercised regarding the constraints inherent in the measurement method. This meta-analysis aimed to determine the left and right hippocampal volume changes and the volume measurement method. Methods: The PubMed, Web of Science, and Scopus databases were searched and published until September 2023. A total of 29 studies were included in this meta-analysis. Results: The meta-analysis results indicate a significant reduction in total hippocampal volume in AD patients. The results for the hippocampal volume on the left and right sides of 3 classes, such as AD, mild cognitive impairment (MCI), and normal control (NC), showed a greater reduction on the left side. The results showed in AD: 95% confidence interval (CI) = −142 to 1.61, P = .04, I ² = 44%; MCI: 95% CI= −186 to −72, P = <.00001, I ² = 0%, and NC: 95% CI = −141 to 14, P = .11, I ² = 80%, respectively, with random effect. The left hippocampal in AD is smaller, while the right hippocampal is larger. The AD class significantly reduced hippocampal volume on both the left and right sides. Conclusion: Notably, automatic segmentation methods impact hippocampal volume measurements, with results potentially relying heavily on their correlation with the manual labeling process.