Gait spatial parameter estimation from smart insoles using deep learning in a large heterogeneous clinical population

While achieving reasonable accuracy, prior smart insole-based approaches for gait parameter estimation have been limited by small sample sizes, narrow clinical settings, and reliance on demographic or anthropometric priors. These limitations restrict their generalizability to heterogeneous real-world populations with diverse gait characteristics. Thus, this study proposes a smart insole-based gait parameter estimation approach that includes diverse health conditions while minimizing feature engineering and eliminating the need for demographic or anthropometric inputs. Using minimally engineered inertial measurement unit signals obtained from smart insoles worn by 702 individuals with diverse neurological and musculoskeletal conditions, a Conformer-based deep learning network was developed to estimate four spatial gait parameters: stride length, step length, stride width, and step width. The performance of the proposed approach was evaluated against the GAITRite walkway system and compared with previously suggested approaches, with additional evaluation using a publicly available dataset. The proposed approach showed excellent agreement with the reference system across walking speeds and evaluation levels. For example, subject-wise estimation of step length at the usual speed achieved a mean absolute error of 1.33 cm (ICC = 0.99). Scatter and Bland-Altman analyses further demonstrated strong agreement between the proposed approach and the reference system. These results highlight the potential of wearable smart insoles combined with deep learning for scalable gait parameter estimation.