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Abstract Background Telerehabilitation is a promising avenue for improving patient outcomes and expanding accessibility. However, there is currently no spine-related assessment for telerehabilitation that covers multiple exercises. Methods We propose a wearable system with two inertial measurement units (IMUs) to identify IMU locations and estimate spine angles for ten commonly prescribed spinal degeneration rehabilitation exercises (supine chin tuck head lift rotation, dead bug unilateral isometric hold, pilates saw, catcow full spine, wall angel, quadruped neck flexion/extension, adductor open book, side plank hip dip, bird dog hip spinal flexion, and windmill single leg). Twelve healthy subjects performed these spine-related exercises, and wearable IMU data were collected for spine angle estimation and IMU location identification. Results Results demonstrated average mean absolute spinal angle estimation errors of 2.59 $$^\circ$$ ∘ and average classification accuracy of 92.97%. The proposed system effectively identified IMU locations and assessed spine-related rehabilitation exercises while demonstrating robustness to individual differences and exercise variations. Conclusion This inexpensive, convenient, and user-friendly approach to spine degeneration rehabilitation could potentially be implemented at home or provide remote assessment, offering a promising avenue to enhance patient outcomes and improve accessibility for spine-related rehabilitation. Trial registration: No. E2021013P in Shanghai Jiao Tong University.

Based on the monitoring data of ambient air quality and meteorological observation data, the characteristics and meteorological influencing factors of air pollution in Luojiang District of Deyang City from 2018 to 2022 were analyzed. The results show that from 2018 to 2022, the main air pollutants affecting the air quality of Luojiang District of Deyang City were PM2.5 and PM10, and the primary pollutant on heavy pollution days was basically PM2.5. PM2.5 and PM10 pollution showed obvious seasonal differences, and PM2S concentration exceeded the limit mainly in spring and winter, among which it was the most serious in early spring, especially in January and February, followed by December. PMW exceeding the standard had a high seasonal correlation with PM2S exceeding the standard, mainly in spring and winter, among which it was the most serious in winter, especially in December, followed by January. PM2.5 and PM1.0 pollution showed an overall weakening trend. PM2.5 and PM1.0 concentration were closely related to meteorological factors such as temperature, relative humidity, wind speed, precipitation and air pressure, and were mainly affected by rainfall. [ABSTRACT FROM AUTHOR]