## 1 Introduction
Inertial Measurement Units (IMUs), consisting of miniaturized accelerometers, gyroscopes, and magnetometers, have emerged as a promising solution. Their portability enables balance assessment in more natural settings, capturing movement data during a wider range of activities in both laboratory and real-world settings, such as residential homes, workplaces, and public spaces [13; 14; 15]. IMUs offer continuous monitoring capabilities, allowing for the detection of subtle balance fluctuations that might be missed during brief, clinically-based assessments. Previous research has demonstrated the feasibility of using IMUs to assess various aspects of balance. [PERSON] (2020) showed that a smartphone-embedded IMU could effectively measure postural stability, with results moderately correlating with gold-standard force platform assessments [13]. In addition, [PERSON] and [PERSON] (2015) explored the use of IMUs combined with machine learning to automatically detect compensatory balance responses during lateral perturbations, achieving high classification accuracy [16]. Moreover, [PERSON] et al. (2018) demonstrated the potential of these devices for estimating segmental contributions to total body angular momentum in individuals with Parkinson's disease, using a limited set of sensors [17]. With a large dataset and machine learning models, postural balance characteristics derived from IMU data were found to be useful. For instance, [PERSON] et al. (2023) [18] were able to evaluate the balance of stroke and Parkinson's patients from placing the IMUs on them while they performed the 6-minute walk test. Also, IMUs may be used complementarily with other sources. A study by [PERSON] (2020) [19] combined IMU and force plate data from 403 elderly participants performing the TUG test and employed machine learning and deep learning models to predict and detect falls among older adults. The research presented in this paper was built upon these promising findings, with the first objective to further explore the potential of IMUs for evaluating postural balance in the elderly during their daily living. Particularly, we developed a straightforward and practical protocol for using IMU sensors, including the steps of calibration, body placement, data recording, and calculation of kinematic features; then, we identified IMU-derived features that are sensitive to variations in postural balance. Our hypothesis was that the features derived from an IMU placed on the lower back will be predictive of fall risk due to its proximity to the body's center of mass and its capacity to capture overall postural oscillations. The placement of IMUs on the body is a critical consideration in balance assessment. Previous studies have explored various sensor locations. Since securing an IMU on the lower back can be challenging in real-world settings as the person may sit or lean on it, it would be interesting to see if we can change the location of a single IMU to another body part. A systematic review by [PERSON] et al. (2019) found that the majority of studies (80.9%) place sensors in the lower back region, particularly at the L5/S1 level, which is close to the body's center of mass ([PERSON] et al., 2020). The use of IMUs in this location was also validated in the real-world setting; for example, among Parkinson's patients living at home ([PERSON] et al., 2020). Besides the L5/S1, [PERSON] and [PERSON] (2019) demonstrated the importance of sacrum-placed sensors in evaluating standing posture and balance ([PERSON] et al., 2020). Their study revealed a strong correlation between the sacrum angle and overall lumbar lordosis, which is important in maintaining postural alignment and stability. As for other locations, [PERSON] et al. (2022) highlighted the relevance of sternum-placed sensors for estimating vertical ground reaction forces during running ([PERSON] et al., 2020). Last but not least, using the head as a reference was proven effective for assessing vestibular disorders ([PERSON] et al., 2020) and vision troubles ([PERSON] et al., 2020).