Scientists Have Created a Comfortable Vest That Will Fix Your Posture

Machine learning allows self-powered fabric to correct posture

Posture is a crucial aspect of health. Long-term improper posture, such as slouching or drooping to one side, can cause pain and discomfort. It has also been linked to an increased risk of cardiovascular illness, eye problems, strokes, and musculoskeletal ailments. Solutions to help persons in improving their posture are necessary to avoid these concerns and improve the health of students and those in sedentary employment. The shortcomings of existing monitoring technology have hampered their widespread implementation. To solve this issue, researchers developed a pleasant, long-lasting self-powered fabric that can be paired with sensors to assist adjust posture in real-time.

The self-powered fabric was created using triboelectric nanogenerators (TENGs), which capture energy from movement to power the posture monitoring sensors. An inbuilt machine learning system analyzes the information acquired by the sensors and may provide quick feedback, notifying the user when their posture needs to be adjusted.

The technology was described in a paper recently published in Nano Research

“People often sit in various poor postures in their daily life, leading to pain and discomfort,” said report author Kai Dong, an associate researcher at the Chinese Academy of Sciences' Beijing Institute of Nanoenergy and Nanosystems.“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.”

The novel fabric is created by knitting a nylon strand and a conductive fiber together. When the user moves, the fibers of the cloth stretch and contract. Contact electrification is a phenomena caused by the ongoing movement and contact of two fibers.

The fabric stretches readily, is sturdy, washable, and breathable, and may be worn for extended periods of time comfortably. As a result, it is perfect for long-term posture monitoring. According to the paper's author, Zhong Lin Wang, the Hightower Chair of the School of Materials Science and Engineering and a Regents' Professor at Georgia Institute of Technology in the United States, aspects like as durability and comfort are crucial in how people utilize smart textiles.

“The flexibility, stretchability, and bending ability all impact the comfort of the wearable sensors,” Wang explained. “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.”
In addition to the fabric's comfort, the dependability of the posture monitoring is critical. The sensors are sewn directly into the cloth at various locations along the cervical, thoracic, and lumbar spines. These postures aid in the collection of data on the most prevalent slouching poses, such as the humpback posture. The sensor data is then analyzed by a machine learning system, which evaluates information about how the user sits, classifies their sitting position, and watches how they adjust their posture when encouraged. This technology correctly detects the wearer's posture 96.6 percent of the time.

Researchers anticipate that by combining wearability with precision, this self-powered monitoring vest can assist students and individuals with sedentary employment in avoiding pain, discomfort, and long-term health concerns. “We believe the TENG-based self-powered monitoring vest offers a reliable healthcare solution for long-term, non-invasive monitoring,” Dong added. “This also widens the application of triboelectric-based wearable electronics.”

The work was supported by the National Key Research and Development Program of China, the National Natural Science Foundation of China, the Beijing Municipal Natural Science Foundation, and the Fundamental Research Funds for Central Universities.

Reference: “Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction” by Yang Jiang, Jie An, Fei Liang, Guoyu Zuo, Jia Yi, Chuan Ning, Hong Zhang, Kai Dong and Zhong Lin Wang, 24 May 2022, Nano Research.

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