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Self-Driven Fabric Paired with Machine Learning Could Help Rectify Posture in Real-Time

Posture is a vital part of health. Continued poor posture, like leaning to one side or slouching, could result in discomfort and pain. It has also proven to intensify the risk of strokes, vision problems, cardiovascular diseases, and musculoskeletal diseases.

Self-Driven Fabric Paired with Machine Learning Could Help Rectify Posture in Real-Time.
This study proposes a portable and convenient self-powered sitting position monitoring vest (SPMV) that reminds users to maintain the right posture during a sustained working period. The SPMV exhibits a high sensitivity, excellent mechanical stretchability, good air permeability, and a posture recognition accuracy of 96.6% using the random forest classifier. Image Credit: Nano Research

Solutions are needed to help people alter their posture to avoid these issues and enhance the health of students and people with deskbound jobs. Since existing monitoring solutions have certain limitations, they have not become popular.

To find a solution to this issue, scientists have created a comfortable and durable self-driven fabric that can be coupled with sensors to help rectify posture in real-time.

The self-driven fabric was created using triboelectric nanogenerators (TENGs), which use movement to gather the energy required to power the posture tracking sensors. The data gathered by the sensors is processed by an incorporated machine learning algorithm that can offer instant feedback, warning the wearer when they have to alter their posture.

The technology was illustrated in an article published in the May 24th issue of Nano Research.

People often sit in various poor postures in their daily life, leading to pain and discomfort. This ‘sitting disease’ could be alleviated if individuals were able to observe their real-time sitting posture by wearing a specific type of clothing made with smart textiles. With the self-powered sitting position monitoring vest we developed, users can watch their posture change on their screen and make necessary adjustments.

Kai Dong, Study Author and Associate Researcher, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences

The dedicated fabric is produced by interweaving a nylon fiber and a conductive fiber together. When the wearer of the fabric moves, the fibers stretch and compress. The nonstop movement and contact between the two fibers generate electricity, an occurrence called contact electrification.

The fabric stretches effortlessly, is durable, breathable, and washable, and can be worn at ease for extended periods. This means that it is suitable for long-term posture tracking. According to the study author Zhong Lin Wang, the Hightower Chair of the School of Materials Science and Engineering and the Regents’ Professor at the Georgia Institute of Technology (USA), factors such as comfort and durability are crucial for how people accept the usage of smart textiles.

The flexibility, stretchability, and bending ability all impact the comfort of the wearable sensors. But these factors also affect how well the fabric works. The fabric exhibits good stretchability due to its knitting structure, which also increases its output and produces a higher voltage.

Zhong Lin Wang, Hightower Chair of the School of Materials Science and Engineering and  Regents’ Professor, Georgia Institute of Technology

Besides fabric comfort, another crucial factor is the reliability of the posture tracking. The sensors are stitched straight into the fabric in positions along the cervical spine, lumbar spine, and thoracic spine. These positions help gather data on the most standard slouching positions, such as the humpback posture.

The data that is gathered by the sensors is then inferred by a machine learning algorithm, which processes information concerning how the wearer is sitting, categorizes their sitting position, and tracks how they rectify their posture when alerted. This platform can accurately identify the posture of the wearer 96.6% of the time.

With this combination of precision and wearability, scientists hope this self-driven monitoring vest will help students, as well as people with deskbound jobs, avoid discomfort, pain, and long-term health issues.

We believe the TENG-based self-powered monitoring vest offers a reliable healthcare solution for long-term, non-invasive monitoring. This also widens the application of triboelectric-based wearable electronics.

Kai Dong, Study Author and Associate Researcher, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences

Other contributors to this study include Yang Jiang, Jie An, Jia Yi, and Chuan Ning of the Beijing Institute of Nanoenergy and Nanosystems at the Chinese Academy of Sciences and the School of Nanoscience and Technology at the University of Chinese Academy of Sciences; Fei Liang at The Hong Kong Polytechnic University; and Guoyu Zuo and Hong Zhang at the Beijing University of Technology.

This research was supported by the National Key R & D Project from the Minister of Science and Technology, the Natural Science Foundation of the Beijing Municipality, the National Natural Science Foundation of China, and the Fundamental Research Funds for the Central Universities.

Journal Reference:

Jiang, Y., et al. (2022) Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction. Nano Research.

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