This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling task. The main goal of this study was to assess the accuracy of the new sensor-to-segment calibration method (denoted as the ‘cycling’ method) by calculating errors in terms of body-segment orientations and 3D knee joint angles using inertial measurement unit (IMU)-based and optoelectronic-based motion capture. To do so, 14 participants were evaluated during pedaling motion at a workload of 100 W, which enabled comparisons of the cycling method with conventional calibration methods commonly employed in gait analysis. The accuracy of the cycling method was comparable to that of other methods concerning the knee flexion/extension angle, and did not exceed 3.8°. However, the cycling method presented the smallest errors for knee internal/external rotation (6.65 ± 1.94°) and abduction/adduction (5.92 ± 2.85°). This study demonstrated that a calibration method based on the completion of a pedaling task combined with a standing posture significantly improved the accuracy of 3D knee joint angle measurement when applied to cycling analysis.

Aim: To evaluate the relationship between the movement abnormalities of the impaired upper limb in children with unilateral cerebral palsy (CP) and bimanual performance.Method: Twenty‐three children with unilateral CP (mean age 11y 10mo [SD 2y 8mo]) underwent evaluation of bimanual performance (Assisting Hand Assessment [AHA]) and a three‐dimensional movement analysis to measure deviations in the movement of their affected upper limb, and compared with 23 typically developing children (TDC) (mean age 11y 11mo [SD 2y 5mo]). Kinematic indices, such as the Global Arm Profile Score (APS), which summarizes the global movement deviation of the upper limb from the norm, and the Global Arm Variable Score (AVS), which represent movement deviations for a given joint, were calculated and correlated to AHA.Results: Values of kinematic indices were significantly higher in children with unilateral CP than in TDC. A strong correlation between Global‐APS and AHA score (r=−0.75) was found. Other significant correlations were found with Global‐AVS, especially in distal joints.Interpretation: Children with unilateral CP had more movement deviations than TDC. The global movement deviation of the impaired upper limb was strongly correlated with bimanual performance. The influence of distal abnormalities confirms the importance of considering these limitations in therapeutics.What this paper adds: Children with unilateral cerebral palsy had more movement deviations than typically developing children in unimanual tasks. A strong relationship was found between movement deviations of the impaired upper limb and bimanual performance.

Background: Freezing of gait (FoG) is one of the most disabling symptoms of Parkinson disease (PD). Detecting and monitoring episodes of FoG are important in the medical follow-up of patients to assess disease progression and functional impact and to adjust treatment accordingly. Although several questionnaires exist, they lack objectivity. Using wearable sensors such as inertial measurement units (IMUs) to detect FoG episodes offers greater objectivity and accuracy. There is no consensus on the number and location of IMU, type of algorithm, and method of triggering and scoring the FoG episodes. Objective: The objective of this study is to investigate the use of multiple wearable sensors sets to detect FoG in patients with PD during various walking tasks under different medication conditions. Methods: This single-center, prospective cohort study (DetectFoG) will include 18 patients with PD. Patients will be fitted with 7 IMUs and will walk a freezing-provoking path under different tasks—“single task,” “dual motor task,” or “dual cognitive task”—and medical conditions corresponding to levodopa medication (“on” or “off”). Passages will be videotaped, and 2 movement disorder specialists will identify FoG episodes in the videos. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the most effective combination of wearable sensors for detecting FoG episodes will be studied. Results: The study is currently in the data collection phase, having commenced recruitment in February 2024. Once all data have been gathered, the data analysis will commence. As of August 2024, 3 patients have been recruited. It is anticipated that the results will be published by the end of 2025. Conclusions: Detecting FoG episodes in various medical and clinical settings would provide a more comprehensive understanding of this phenomenon. Furthermore, it would enable reliable and objective monitoring of the progression of this symptom based on treatments and the natural course of the disease. This could serve as an objective tool for monitoring patients and assessing the severity and frequency of FoG. Trial Registration: Clinicaltrials.gov NCT05822258; https://www.clinicaltrials.gov/study/NCT05822258 International Registered Report Identifier (IRRID): DERR1-10.2196/58612