## 2 Technological Aspects of Smart Shoes for Digital Health
### Analysis Methods  
Once the sensor data are acquired, informative and relevant parameters must be computed to derive the necessary information relevant to important sports, health, and medical events (Figure 4). First, the computed parameters should be able to facilitate an objective assessment of user performance and mobility status. Second, the parameters should be determined with sufficient accuracy and reliability for use in assessment by domain experts. Therefore, the development of analysis methods should be driven by two inherently different concepts, defining: (i) what would be useful to measure from a medical point of view; and (ii) how these measures can be obtained from an engineering point of view. The development of analysis methods is further driven by the application context. For smart shoes specifically, this paper discusses algorithms for two major areas of applications: (1) activity pattern recognition for applications in sports and healthy living; and (2) gait signature derivation for medical diagnostic and treatment contexts. First, **activity patterns** (Figure 4, left) provide qualitative information about daily physical activities, such as type, duration, or temporal structure, enabling the assessment of gross changes in the engaged activities or mobility status. Activity pattern computation only requires basic signal processing with no a priori information regarding the subject or the performed activity. Most activity pattern computation can be broadly applied without the need for individualization or for the targeting of specific tasks. Consequently, activity patterns are not suitable for the identification of distinct motions or disease-related gait patterns and characteristics. Figure 4 (left) provides an exemplary illustration of an \"activity barcode\" that characterizes one's physical activity patterns over time [26]. Second, **gait signatures** (Figure 4, right) allow the computation of quantitative parameters of specific motions, enabling a more detailed analysis of motion patterns (including variability) [27] and mobility status [28]. Gait signature computation requires tailored signal processing and prior knowledge of the biomechanics of specific movements. It often benefits from a priori knowledge about the subject's biomechanical (anatomical) information and/or the performed activity profiles, e.g., an incorporation of subject-, activity-, or disease-specific information. Nevertheless, this implies a restriction to specific, well-understood disease conditions, which increases the amount of resources required for developing and validating the respective algorithms. As an example, [PERSON] et al. used a smart shoe gait signature analysis system to objectively measure the gait parameters of geriatric  
Figure 3: Practical use of smart shoes within the IoHT: capturing gait signals of patients with impaired gait (daytime use), data storage, and battery charging (nighttime use). Icons modified and printed with permission of [3; 4; 5]. Patient figure adapted from [6]. patients with and without support of a wheeled walker, aimed at evaluating the individual therapy effect caused by this walking aid [29]. In this section, we provide an overview of concrete analysis methods that are relevant for both activity pattern and gait signature computation. As outlined in an excellent review article by [30], there is a common pipeline for inertial sensor-based gait analysis (reproduced and adapted in Figure 5). We will focus on arguably the most important pipeline steps: preprocessing, segmentation, estimation of gait patterns, and recognition of, for example, the clinically applicable mobility status. Figure 4: **(Left)**: A schematic illustration of activity pattern variation over time, which is often referred to as an activity barcode [26]; barcode printed with permission of the authors. (**Right**): assessment of clinically relevant gait signatures [27], e.g., gait cycle length as illustrated in the figure.