Children's walking patterns are distinctive from adults and evolve with age (Senden et al., 2023, Cigali et al., 2011). At a small age, gait tends to be less repetitive and will differ from those of adults (Jain et al., 2016, Ganley&Powers, 2005), emphasizing the need to build normative data for a paediatric population (Ganley&Powers, 2005). The differences in school-aged children's walking patterns are often attributed to their significant musculoskeletal changes (Bari et al., 2023), given that they are in a critical stage of growth and development (Onis et al., 2007). A recent study (Bach et al., 2021) suggested that the degree of gait maturity does not always directly relate to the chronological age of the child. This finding underscores the complexity of assessing gait development in children. Moreover, it's been shown that, compared to adults, young individuals exhibit more variable kinematic patterns when performing repetitive movements (Kuhtz-Buschbeck et al., 1996). Additionally, the observed variability in Electromyography (EMG) waveform within-session for children exhibited approximately twice the variability of EMG signals (muscle activation level) for adults (Granata et al., 2005), affecting their joint kinematics and kinetics within a single session.
Three-dimensional gait analysis (3DGA) is a valuable tool for understanding a child's gait pattern and how it compares with normative data of typically developed children's gait (Bari et al., 2023, Ito et al., 2022). The insights gained from 3DGA in children affected by lower limb movement disorders serve as a foundation for clinical assessment to target personalized treatment and improve their walking patterns (Bari et al., 2023). Considering the unique challenges and broad spectrum of motor impairments in this population, addressing developmental challenges requires a tailored approach. The current gold standard for performing 3DGA involves Optical Motion Capture (OMC) along with force plates due to its high accuracy and robustness (Chester et al., 2005). However, the high cost of OMC systems and the time-consuming data post-processing needed lead to long waitlists for patients and sometimes long-distance travel for families coming from rural areas (Aminian&Najafi, 2004).
Wearable sensors like Inertial Measurement Units (IMUs) are potential alternatives to the OMC system, enabling the potential to capture 3DGA in rural areas and natural environments (Gurchiek et al., 2019). Unlike the OMC systems, IMUs are inexpensive, small, and lightweight and can be used outside the clinic by wearing them or attaching them to the children's limbs or pelvis (Aminian&Najafi, 2004). Although IMU sensors are very promising in motion analysis, challenges such as time-increasing drift errors, which result in less accurate estimations, still need to be overcome (Aminian&Najafi, 2004). Moreover, traditional approaches (e.g., sensor fusion algorithms (Sabatini, 2006, Madgwick et al., 2011)), as well as the tool package OpenSense (Al Borno et al., 2022), require placing an IMU on each body segment for accurate kinematics calculations and functional calibration.
The challenges associated with processing IMU data in adult populations have been addressed in previous studies (Moghadam et al., 2023a, Dorschky et al., 2020, Mundt et al., 2021, Tan et al., 2022, Findlow et al., 2008, Sharifi Renani et al., 2021, Lim et al., 2020, Giarmatzis et al., 2020, Luu et al., 2014) by implementing Machine learning (ML) models. These ML models can establish a direct relationship between the IMUs' data and OMC derived gait time series (joint kinematics, joint kinetics, and muscle forces) (Moghadam et al., 2023a). Prior research indicated the efficacy of this approach in adult populations, demonstrating highly accurate results with low errors during personalized model (tested on the same individual used for training). Additionally, reliable estimations were yielded using generalized models (tested on new participants not included in the training set), even in scenarios with limited dataset availability. Among various ML models developed for adults’ population, artificial neural networks (ANN) have been widely utilized for predicting gait time series. However, there is a limited body of literature exploring alternative data-driven models that may demand smaller datasets while achieving comparable results to ANNs. Building on this context, in a prior study, we demonstrated that Random Forest (RF) models can yield results comparable to more intricate machine learning models such as Convolutional Neural Networks (CNNs) for 3D Gait Analysis (3DGA) in adults (Moghadam et al., 2023a). Given the greater heterogeneity in children's gait, it will be interesting to explore whether RF or CNNs can be applied to a paediatric population with similar performances.
The primary focus of existing ML models for 3DGA in children lies in gait classification (Khaksar et al., 2021, Zhang&Ma, 2019, Zhang et al., 2009, Kamruzzaman&Begg, 2006, Choisne et al., 2020) rather than the development of models for predicting gait time series. There are only a handful of studies focused on predicting children’s gait using ML techniques (Kolaghassi et al., 2023, Morbidoni et al., 2021, Kolaghassi et al., 2022, Kwon et al., 2012, Vigneron et al., 2017). A research group used EMG sensors’ signals to predict children with cerebral palsy (CP) knee moment and achieved high correlation coefficients between 0.71 and 0.93 for different participants (Kwon et al., 2012). Another study proved the feasibility of using neural networks in predicting gait events from surface EMG signals in hemiplegic cerebral palsy (Morbidoni et al., 2021). Other studies have employed ML techniques to estimate one-step-ahead gait trajectories to control lower-limb robotic devices in children with CP (Kolaghassi et al., 2023, Kolaghassi et al., 2022). However, none of the mentioned studies utilized IMUs’ data to develop the ML model. Given the effective performance of a combination of IMU and ML models in adults, exploring its applicability in a heterogenous paediatric population would be an interesting avenue for investigation.
It is noteworthy that previous studies have indicated the feasibility of predicting diverse gait time series in adults using a single IMU on the pelvis (Lim et al., 2020) or a pair of IMUs on the shanks (Sharifi Renani et al., 2020, Yeung et al., 2023) or the feet (Gholami et al., 2020). However, given the unique challenges posed by children's gait, it remains crucial to extend this inquiry for children gait analysis by exploring the applicability of using a reduced number of IMUs. A high number of IMU sensors on the body could be impractical in real-world gait analysis, particularly for at-home applications, as it requires high computational power to monitor numerous IMUs (Sivakumar et al., 2019). Therefore, another aspect requiring investigation is to quantify the optimal number of IMUs needed for accurately estimating gait time series in children.
Therefore, this study aimed to assess the feasibility of leveraging data from IMUs to construct ML models for predicting gait time series in school-aged children. This goal was pursued through three key objectives. Firstly, we sought to explore whether personalized and generalized ML models for predicting gait time series in children could demonstrate comparable efficacy to their adult counterparts. Secondly, an evaluation was conducted to compare the accuracy of two distinct ML models – the multi-output RF and CNN models – for predicting gait time series in children. The final objective centred on exploring the potential of placing a singular IMU on each foot, as opposed to employing seven IMUs distributed across all lower limb segments.