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Researchers at the Indian Institute of Technology Delhi (IIT-Delhi) have developed a scalable wearable pressure sensor that can help doctors and specialists analyse gait patterns and postural deformities. The sensor was created using a nanocomposite material, boasting a unique combination of light-sensitive polymer and piezoelectric nanoparticles.
Gait and postural deformities are prevalent and debilitating conditions. The most prominent deformities include splay foot, flat foot, unstable hind foot with protruding heels, high arches, and irregular gait. These conditions are linked to issues like impaired balance, abnormal posture, swollen knees, and weakened joints, leading to walking difficulties.
It is crucial to adequately monitor these deformities as it can help detect underlying medical conditions and accelerate the patient’s healing process, preventing long-term harm.
In the IIT-Delhi study, the sensor is fully flexible and can be employed as a sensor array because of its robust design that comfortably fits inside various insole sizes. It can also be easily attached to a palm or any other body part where localised pressure sensing may be useful.
The use of a dual transduction nanocomposite material in the sensor enables the simultaneous detection of both mechanical strain and contact force or pressure. This feature facilitates seamless integration with current machine learning algorithms by providing higher feature elements for analysis.
The output generated by the sensor is analysed by conventional machine learning models, which are then correlated with an individual’s walking behaviour. By comparing the pressure patterns to pre-defined patterns of a typical, healthy person, clinical specialists can deduce the specific type of deformity that may be present.
According to lead researcher Dhiman Mallick, the integration of sensors and machine learning can lead to the creation of intelligent sensors for healthcare, sports science, and defence. “During the number of tests that we conducted in our laboratory, we found that the proposed sensor can potentially help detect foot problems in adults and children by analysing the pressure variation on the back end of the foot and converting it into electrical output. Since abnormal hind foot pressure distribution can lead to problems in knee joints, hips, and even spine-related injuries, understanding and correcting it is an important application”, he explained.
The pressure patterns obtained can be a valuable tool for doctors and specialists when creating customised insoles. These insoles are designed to address and balance out foot deformities by providing support to the areas of the foot with abnormal pressure distribution. As a result, the sensor could offer an easy, cost-effective alternative to expensive footwear modifications, surgical interventions, and posture correction accessories.
The sensor can also discern various human activities like walking, running, or other actions, by detecting pressure changes in the user’s hind foot. It analyses the variability in foot pressure during different biomechanical movements, allowing it to associate specific pressure patterns with particular activities.
The technology holds significant potential in smart healthcare systems, where it can analyse crucial health parameters such as activity patterns, exercise intensity, and step count, particularly for people with diabetes and obesity. Furthermore, the sensor’s capabilities are valuable for detecting falls among the elderly, especially in those with Parkinson’s disease or who are disabled.
Moreover, due to the versatility of the sensor, it could be used in injury rehabilitation. For example, it has been applied to evaluate hand grip strength, a critical aspect in understanding recovery, particularly in cases of limb or hand injuries. The strength of the grip directly reflects the progress of healing in such injury scenarios.
The IIT-Delhi study titled ‘Machine Learning Assisted Hybrid Transduction Nanocomposite Based Flexible Pressure Sensor Matrix for Human Gait Analysis’ has been published in Nano Energy, a leading nanotechnology journal.