A Systematic Review on Smart Insole Prototypes: Development and Optimization Pathways

Injury prevention in sports remains a perpetual challenge, frequently hindered by linear approaches that fail to account for the complexity of real-world scenarios. We propose a paradigm shift in this perspective, framing injury prevention as a 'wicked problem' characterised by evolving, context-dependent factors and competing priorities. We suggest that while eliminating injuries entirely may not be feasible, their impact can be effectively reduced through tailored, adaptable solutions. We illustrate the practical application of this framework through a case study of Cirque du Soleil. By adopting a revised 'Sense-Think-Act' paradigm, interventions were collaboratively developed with performers, medical personnel and organisational leaders. This resulted in a 15% reduction in injuries and substantial decreases in missed performances and overuse injuries over 4 years. The success of this approach can be attributed to context-sensitive strategies that effectively balance injury risk mitigation with performance objectives. Our proposed view challenges traditional definitions of success in injury prevention, that is, developing 'optimal solutions' instead of 'ultimate solutions'. By rethinking injury prevention as a wicked problem, we nudge sports professionals to adopt flexible, sustainable practices that address the dynamic realities of their environments. Such a shift enhances injury prevention outcomes and aligns these efforts with the broader priorities of athletic communities.

Objective:This manuscript aims to provide a review and synthesis of contemporary advancements in footwear, sensor technology for remote monitoring, and digital health, with a focus on improving offloading and measuring and enhancing adherence to offloading in diabetic foot care.Methods:A narrative literature review was conducted by sourcing peer-reviewed articles, clinical studies, and technological innovations. This paper includes a review of various strategies, from specifically designed footwear, smart insoles and boots to using digital health interventions, which aim to offload plantar pressure and help prevent and manage wounds more effectively by improving the adherence to such offloading.Results:In-house specially made footwear, sensor technologies remotely measuring pressure and weight-bearing activity, exemplified for example, through applications like smart insoles and SmartBoot, and other digital health technologies, show promise in improving offloading and changing patient behaviour towards improving adherence to offloading and facilitating personalised care. This paper introduces the concept of gamification and emotive visual indicators as novel methods to enhance patient engagement. It further discusses the transformative role of digital health technologies in the modern era.Conclusions:The integration of technology with footwear and offloading devices offers unparallelled opportunities for improving diabetic foot disease management not only through better offloading but also through improved adherence to offloading. These advancements allow healthcare providers to personalise treatment plans more effectively, thereby promising a major improvement in patient outcomes in diabetic foot ulcer healing and prevention.

Diabetic foot is a common and debilitating complication of diabetes that significantly impacts patients' quality of life and frequently leads to amputation. In parallel, artificial intelligence (AI), particularly machine learning (ML), has emerged as a powerful tool in healthcare, offering novel solutions for disease prediction, monitoring, and management. Despite growing interest, a systematic overview of machine learning applications in diabetic foot research is still lacking. This study aims to systematically analyze recent literature to identify key trends, focus areas, and methodological approaches in the application of machine learning to diabetic foot research. A comprehensive literature search was conducted across three major databases: Web of Science (WoS), IEEE Xplore, and PubMed. The search targeted peer-reviewed journal articles published between 2020 and 2024 that focused on the intersection of machine learning and diabetic foot management. Articles were included if they were indexed in the Science Citation Index (SCI) or Social Sciences Citation Index (SSCI), published in English. They explored the use of machine learning in diabetic foot-related applications. After removing duplicates and irrelevant entries, 25 original research articles were included for review. There has been a steady increase in publications related to machine learning in diabetic foot research over the past 5 years. Among the 25 studies included, image analysis was the most prevalent theme (12 articles), dominated by thermal imaging applications (10 articles). General clinical imaging was less common (2 articles). Seven studies focused on structured clinical data analysis, while six explored IoT-based approaches such as smart insoles with integrated sensors for real-time foot monitoring. Citation analysis showed that Computers in Biology and Medicine and Sensors had the highest average citation rates among journals publishing multiple relevant studies. The integration of machine learning into diabetic foot research is rapidly evolving; it is characterized by growing diversity in data modalities and analytical techniques. Thermal imaging remains a key area of interest, while IoT innovations show promise for clinical translation. Future studies should aim to incorporate deep learning, genomic data, and large language models to further enhance the scope and clinical utility of diabetic foot research.

The first step in identifying risk factors for injuries is to characterize the myoelectric activity of different muscles after ground contact, especially when fatigue is a limiting factor. This study aimed at recording the myoelectric activity of calf muscles after ground contact during different types of jumps and investigating the effect of motor learning and fatigue on muscle preactivation. Twenty four male students aged 24.3±1.2 years old performed three different motor activities: A) jump from a box with counter landing (JCL) on 30x30 cm plate; B) drop jump with bounce drop jump (BDJ); and C) BDJ followed by a jump on 51-cm step. The surface electromyography was used to examine the following muscles: m. tibialis anterior (TA), m. gastrocnemius medialis, m. gastrocnemius lateralis, and m. soleus (SO). The measurements were taken during different jumps before and after motor learning and fatigue stimulus. There were significant differences in preactivation for TA between JCL and BDJ followed by a jump under the influence of fatigue (P<0.05). The differences were observed also during BDJ between non-fatigued and fatigued conditions. There was a statistically significant difference for GL between BDJ pre- and postmovement motor learning and BDJ pre- and postfatigue influence. Current results indicate that myoelectric activity of muscles during motor activities is different, and the effect of motor learning and fatigue was shown. Thus, it could be important in the injury prevention in sport.

Predictive analytics, strategic game analysis, and injury prevention in sports: the role of big data and artificial intelligence

Data-Driven Strategies for Injury Prevention in Sports: Integrating Biomechanics and Wearable Analytics

Athletes can also cause damage to some parts of their body during training, so specialized preparation activities should be carried out before athlete training to reduce the damage caused to the athlete's body, allowing the stressed parts to move and distribute the load. Excessive recovery has a significant effect on improving the performance level of the athletes studied and preventing sports injuries. This article studies the data analysis of body recovery and injury prevention in physical education teaching based on wearable devices. Real time collection of students' exercise data, including indicators such as exercise volume, heart rate, steps, distance, etc., by wearing wearable devices. By using Internet of Things technology to transmit data to cloud servers, data analysis and mining techniques are used to process the data and study issues related to body recovery and injury prevention. Specifically, this article adopts methods such as time series analysis, machine learning algorithms, and artificial neural networks to analyze the relationship between exercise data and body recovery and injury prevention, providing scientific guidance and support for physical education teaching. This method can monitor students' exercise data in real-time, predict the risk of physical recovery and injury, and provide corresponding prevention and guidance suggestions.

Injury Prevention in Sports

OARSI Clinical Trials Recommendations: Design and conduct of clinical trials for primary prevention of osteoarthritis by joint injury prevention in sport and recreation

Measuring joint moments at high speed is integral for sports-related research, but substantial artefacts can arise with certain computational methods. This is relevant for BJSM readers as measurement errors can...

The purpose of this study was to investigate the effect that difference in forefoot of shoe flexibility during the quick lunge from a jump smashing on the lower limbs and the plantar pressure distribution. For this 10 elite badminton players with over 10 years experience and right handed participated. Two kinds of badminton shoes were selected and tested mechanical testing for the forefoot flexibility. Motion analysis, ground reaction forces and plantar pressure distribution were recorded. It was required to conduct lunge movement after jumping smashing as possible as high. Photo sensor was located in 3 meter away from standing position and its height was 40 cm. Subjects were conducted to return original position after touching the sensor as under clear movement as possible as fast. Forefoot stiffness had an effect on shoe peak bending degree and peak bending angular velocity in propulsion phase. Forefoot flexibility had an effect on ankle plantar flexion and knee flexion moment. It appears that joint power on lower limb and peak plantar pressure were not influenced by the flexibility of shoes.

Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.

Objective: analyze alterations in plantar pressure distribution and balance in young adults diagnosed with a complete ACL rupture, as well as to evaluate the impact of these conditions on athletic performance. Methodology: This study involved 34 young adults, divided into two groups based on the presence or absence of a complete Anterior Cruciate Ligament (ACL) rupture. The study’s methodology included specific inclusion and exclusion criteria, ethical considerations, an experimental protocol involving plantar pressure distribution and bipodal open-eye postural control assessments, and statistical analysis using Minitab 21 software. The plantar pressure was recorded using a Baroscan Podotech platform, and the displacement amplitude of the center of pressure (COP) was evaluated in both anteroposterior (COPAP) and mediolateral (COPML) directions. The results were analyzed for normality, homogeneity, and differences within the group, with a p-value ≤ 0.05 considered statistically significant. Results: In this study, all participants in Group A, who all had an injury to the right knee, served as a crucial criterion for their inclusion and an important reference point for the analysis. The Center of Pressure Anteroposterior (COPAP) measurement in Group A was 17.79 cm (±0.82), while in Group B, this measurement was significantly higher, at 25.10 cm (±1.04). The difference between the two groups was statistically significant (p=0.01). As for the Center of Pressure Medial-lateral (COPML) measurement, it was 14.47 cm (±9.99) in Group A and 10.80 cm (±0.69) in Group B. Again, the difference between the two groups was statistically significant (p=0.01). Conclusion: The study concludes that a complete rupture of the Anterior Cruciate Ligament (ACL) significantly influences the Anteroposterior (COPAP) and Mediolateral (COPML) Center of Pressure measures but may have less impact on plantar pressure distribution. A trend of higher pressure in the Forefoot compared to the Rearfoot was observed in both groups, possibly due to biomechanical changes following an ACL rupture. These findings, which align with recent studies, suggest that while plantar pressure and center of pressure measurements are important for diagnosing foot problems and monitoring gait and posture, an ACL rupture may have a less pronounced impact on plantar pressure distribution. Further research is needed, but these insights could have important implications for the rehabilitation and management of individuals with an ACL rupture.

Foot ulcer is a severe complication affecting about 25% of diabetes mellitus patients due to a lower blood supply and loss of foot sensitivity (neuropathy). A fast and reliable identification of foot pressure loads and temperature distributions changes on the plantar surface allows to prevent and reduce the consequences of ulceration such as foot or leg amputation. Several wearable technologies have been developed and tested by the scientific community, addressing the “diabetic foot” topic. However, the dimensions of the devices and the combined pressure/temperature monitoring capabilities don’t accommodate the requirements from both the end-users and caregivers: normally just one information—pressure loads or temperature map—is acquired, moreover the amount of thermal reading points is lower than 5 and the accuracy of thermal sensors is greater than 0.5 °C. This work presents a smart insole in which both temperature and pressure data in 8 reading points are monitored in remote way for the assessment of the health foot conditions by a caregiver. Minimally invasive and low power temperature and force sensors have been chosen and integrated into two antibacterial polyurethane-based layers architecture, designed in accordance with the typical requirements of diabetic foot. Based on the results, the developed system shows high performance in terms of temperature and pressure detection.

Integrated kinematics–kinetics–plantar pressure data analysis: A useful tool for characterizing diabetic foot biomechanics