Research on a Task-Driven Classification and Evaluation Framework for Intelligent Massage Systems

Wang, Lingyu; Wang, Junliang; Guo, Meixing; Liu, Guangtao; Fang, Mingzhu; Yan, Xingyun; Wang, Hairui; Chen, Bin; Zhu, Yuanyuan; Hu, Jie; Qi, Jin

doi:10.3390/app15179327

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Open AccessArticle

Research on a Task-Driven Classification and Evaluation Framework for Intelligent Massage Systems

by

Lingyu Wang

¹,

Junliang Wang

²,

Meixing Guo

¹,

Guangtao Liu

²,

Mingzhu Fang

¹,

Xingyun Yan

¹,

Hairui Wang

¹,

Bin Chen

¹,

Yuanyuan Zhu

³,

Jie Hu

^4,* and

Jin Qi

^4,*

¹

School of Design, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai 200240, China

²

Shanghai Rongtai Health Technology Co., Ltd., No. 1226, Zhufeng Road, Qingpu District, Shanghai 201702, China

³

School of Art and Design, Nanjing Forestry University, No. 159, Longpan Road, Xuanwu District, Nanjing 210037, China

⁴

School of Mechanical and Power Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai 200240, China

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2025, 15(17), 9327; https://doi.org/10.3390/app15179327 (registering DOI)

Submission received: 14 July 2025 / Revised: 16 August 2025 / Accepted: 22 August 2025 / Published: 25 August 2025

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Abstract

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection.

Keywords: intelligent massage systems (IMS); capability grading; task-driven; Massage-Driven Task (MDT) model; performance indicators

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MDPI and ACS Style

Wang, L.; Wang, J.; Guo, M.; Liu, G.; Fang, M.; Yan, X.; Wang, H.; Chen, B.; Zhu, Y.; Hu, J.; et al. Research on a Task-Driven Classification and Evaluation Framework for Intelligent Massage Systems. Appl. Sci. 2025, 15, 9327. https://doi.org/10.3390/app15179327

AMA Style

Wang L, Wang J, Guo M, Liu G, Fang M, Yan X, Wang H, Chen B, Zhu Y, Hu J, et al. Research on a Task-Driven Classification and Evaluation Framework for Intelligent Massage Systems. Applied Sciences. 2025; 15(17):9327. https://doi.org/10.3390/app15179327

Chicago/Turabian Style

Wang, Lingyu, Junliang Wang, Meixing Guo, Guangtao Liu, Mingzhu Fang, Xingyun Yan, Hairui Wang, Bin Chen, Yuanyuan Zhu, Jie Hu, and et al. 2025. "Research on a Task-Driven Classification and Evaluation Framework for Intelligent Massage Systems" Applied Sciences 15, no. 17: 9327. https://doi.org/10.3390/app15179327

APA Style

Wang, L., Wang, J., Guo, M., Liu, G., Fang, M., Yan, X., Wang, H., Chen, B., Zhu, Y., Hu, J., & Qi, J. (2025). Research on a Task-Driven Classification and Evaluation Framework for Intelligent Massage Systems. Applied Sciences, 15(17), 9327. https://doi.org/10.3390/app15179327

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Research on a Task-Driven Classification and Evaluation Framework for Intelligent Massage Systems

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