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However, most existing soft exosuit systems rely on preset assistance modes, which may not accurately align with individual physiological states and movement requirements, leading to variable user experiences and efficacy. While existing human-in-the-loop (HIL) research predominantly focuses on optimizing metabolic cost and torque difference parameters, there is a notable absence of real-time monitoring methods that closely reflect the human body\u2019s physiological state and strategies that dynamically indicate walking efficiency. Motivated by this, we developed a novel personalized power-assist system. This system optimizes the power-assist output of the hip joint by monitoring the user\u2019s physiological and motion signals in real time, including heart rate (HR), blood oxygen saturation (SpO2), and inertial measurement unit (IMU) data, to assist hip flexion based on feedback. The findings from a metabolic expenditure trial demonstrate that the innovative soft exosuit, which is based on a Physiological State Monitoring Control (PSMC) system, achieves a reduction of 7.81% in metabolic expenditure during treadmill walking at a speed of 3.5 km\/h compared to walking without the assistance of the exosuit. Additionally, during continuous exercise with varying intensities, the metabolic consumption level is reduced by 5.1%, 5.8%, and 8.2% at speeds of 2, 4, and 6 km per hour, respectively. These results support the design of a novel hip flexion-assisting soft exosuit, demonstrating that applying different assistance forces in consideration of different physiological states is a reasonable approach to reducing metabolic consumption.<\/jats:p>","DOI":"10.3390\/bios14090418","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T11:58:46Z","timestamp":1724759926000},"page":"418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Personalized Strategy for Hip Joint Flexion Assistance Based on Human Physiological State"],"prefix":"10.3390","volume":"14","author":[{"given":"Beixian","family":"Wu","sequence":"first","affiliation":[{"name":"College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, China"},{"name":"ShenZhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4855-9914","authenticated-orcid":false,"given":"Chunjie","family":"Chen","sequence":"additional","affiliation":[{"name":"ShenZhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"ShenZhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, 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