Insole-Based Estimation of Vertical Ground Reaction Force Using One-Step Learning With Probabilistic Regression and Data Augmentation

Musculoskeletal Rehabilitation: New Perspectives in Postoperative Care Following Total Knee Arthroplasty Using an External Motion Sensor and a Smartphone Application for Remote Monitoring

BACKGROUND

The number of total knee replacements performed annually is steadily increasing. Parallel options for postoperative care are decreasing, which reduces patient satisfaction. External devices to support physical rehabilitation and health monitoring will improve patient satisfaction and postoperative care.

METHODS

In a prospective, international multicenter study, patients were asked to use an external motion sensor and a smartphone application during the postoperative course of primary total knee arthroplasty. The collected data were transferred to a data platform, allowing for the real-time evaluation of patient data.

RESULTS

In three participating centers, 98 patients were included. The general acceptance of using the sensor and app was high, with an overall compliance in study participation rate of up to 76%. The early results showed a significant improvement in the overall quality of life (p < 0.001) and significant reductions in pain (p < 0.01) and depression (p < 0.001).

CONCLUSIONS

The early results of this clinical and multicenter study emphasize that there is a high interest in and acceptance of digital solutions in patients' treatment pathways. Motion sensor and smartphone applications support patients in early rehabilitation.

Prediction of Three-Directional Ground Reaction Forces during Walking Using a Shoe Sole Sensor System and Machine Learning

We developed a shoe sole sensor system with four high-capacity, compact triaxial force sensors using a nitrogen added chromium strain-sensitive thin film mounted on the sole of a shoe. Walking experiments were performed, including straight walking and turning (side-step and cross-step turning), in six healthy young male participants and two healthy young female participants wearing the sole sensor system. A regression model to predict three-directional ground reaction forces (GRFs) from force sensor outputs was created using multiple linear regression and Gaussian process regression (GPR). The predicted GRF values were compared with the GRF values measured with a force plate. In the model trained on data from the straight walking and turning trials, the percent root-mean-square error (%RMSE) for predicting the GRFs in the anteroposterior and vertical directions was less than 15%, except for the GRF in the mediolateral direction. The model trained separately for straight walking, side-step turning, and cross-step turning showed a %RMSE of less than 15% in all directions in the GPR model, which is considered accurate for practical use.

INSOLE-BASED ESTIMATION OF COMPLETE GROUND REACTION FORCE WITH GAUSSIAN KERNEL REGRESSION AND DATA EXPANSION

A method for estimating ground reaction force (GRF) with plantar pressure was proposed in this paper. The estimation model was constructed to approximate the nonlinear relationships between GRF and the plantar pressure according to the linear combinations of Gaussian kernel functions. Partial least squares regression (PLSR) was adopted to obtain model parameters and eliminate multicollinearity among the pressure components. The general model and subject-specific models were constructed for 12 male and 4 female subjects. Moreover, a data expansion method was introduced for the establishment of subject-specific model, which is implemented by searching and adopting the data with consistent statistical characteristics in a pre-established database. That approach is particularly meaningful for the group whose walking ability is limited or clinic where the force platform is not available. The NRMSEs (%) for general model were 5.27–7.85% (GRF_V), 7.35–8.53% (GRF_ML), and 8.82–10.54% (GRF_AP). The maximum NRMSEs (%) for subject-specific models were 5.02% (GRF_V), 9.91% (GRF_ML), and 10.23% (GRF_AP). Results showed that both general and subject-specific models achieved higher accuracy than existing methods such as linear regression and neural network methods.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review

The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.