RESUMO
ObjectiveTo compare the retest reliability and discriminant validity of dynamic postural stability indices for functional ankle instability (FAI) obtained by different algorithms based on acceleration signals at different positions of human body. MethodsFrom April to June, 2021, 21 subjects with unilateral FAI and 21 subjects with normal ankle were recruited. Three inertial sensors were attached to the waist points, knee and ankle positions. The ground reaction force (GRF) and kinematics data of the subjects in multi-direction single leg landing test were collected synchronously by 3D force plate and inertial sensors. The unbounded third order polynomial (UTOP) fitting method was used to calculate the stability time, and the root mean square was used to caculate the stability index. ResultsMost of the indicators calculated based on acceleration signal correlated with that based on GRF with low coefficient (|r| = 0.116 to 0.368, P < 0.05). The stability time and stability index based on the acceleration signals of different positions of human body showed low to high retest reliability (CMC 0.30 to 0.91). For the females, among the stability time based on acceleration signal, eleven indexes achieved average to very high discriminant validity (AUC = 0.702 to 0.942, P < 0.05); eight of the stability indexes reached general level of discriminant validity (AUC = 0.717 to 0.782, P < 0.05). No algorithms achieved good discriminant effect in male subjects. ConclusionBased on the acceleration signal of waist point in single-leg landing stability test, the stability time calculated by UTOP algorithm can evaluate the dynamic postural stability of female FAI patients with high discriminant validity and medium to high retest reliability.
RESUMO
Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: -0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.