## 2 Technological Aspects of Smart Shoes for Digital Health
### Application Examples
#### 2.3.2 Medical Applications  
Medical applications target the heavily regulated, patient-oriented, high-accuracy medical devices market. Most applications that are based on smart shoe technologies aim for impairment-specific gait signature assessment. A recent review article [25] gives an excellent overview regarding the applications of smart shoes in the medical domain, including fantastic overview tables that compare related work with specific items. In their study, the authors found that 29% of the included articles address Parkinson's disease, 17% Cerebral Palsy, and 13% orthoses, as summarized in Figure 6.  
In [28], [PERSON] et al. integrated clinical gait assessment in a smart shoe for the machine learning-based discrimination of healthy controls and patients in increasing stages of Parkinson's disease (PD). The results proved that discrimination using these objective measurements is feasible. PD has also been in the focus of different review articles [72; 73] discussing the applicability and possibilities of especially wearable monitoring technologies. A reason for this is that PD is an excellent model disease for the development of mHealth technologies [74], where the results will be generalizable to a variety of other neurological and musculoskeletal conditions [75]. Wearables' application to PD was also investigated in an excellent article by [PERSON] and colleagues [76]. [PERSON] et al. introduced a real-time system that could provide visual feedback to improve postural stability in PD [77]. Feedback was given in the form of depicting the geometry of the patient's body. The authors recruited 17 patients with PD, and tested their balance after pull-tests under various conditions, e.g., eyes closed/open with or without visual biofeedback. A comparable study by [PERSON] et al. examined the influence of smart shoes on stability and gait rehabilitation [78]. The authors highlighted the potential to individualize the diagnosis of gait impairment and the adaptation of treatment to the specific difficulties of each subject. According to the authors, it is crucial that therapists recognize the benefits and limitations of technology-enriched therapy. They also advocated that further steps towards telerehabilitation and digital health would be beneficial. In [79], [PERSON] et al. targeted the discrimination of frailty status in geriatric patients, i.e., non-frail, pre-frail, and frail. The study was based on data collected for the Arizona frailty cohort, and employed a wearable sensor-based at-home assessment of gait, balance, and physical activity. Their results showed that stride length and double support time were the most sensitive parameters for discriminating the three conditions. [PERSON] et al. validated the use of smart shoes during a 10-m walking test to quantify the functional level in individuals with spinal cord disorders [80] and to predict their postoperative outcomes [81]. Their results support the view that the measurement of spatiotemporal gait characteristics from smart shoes can provide an accurate representation of functional level and predictions of surgical outcomes. A different application was addressed by [PERSON] et al. in [82]. The authors focused on diabetics at risk of suffering foot ulcers. The authors designed a wireless, customizable, and washable smart sock with pressure-sensitive fibers that computed stress indicators based on pressure data. A subject-specific finite element foot model was introduced to provide real-time feedback to help patients prevent foot ulcers. A further aspect that has been researched is glaucoma patients. [PERSON] and colleagues [83] investigated gait function in this patient population, and presented a wearable wireless sensor system designed for real-time gait pattern analysis in glaucoma patients. The system was utilized in a randomized clinical trial with 19 participants, both glaucoma patients and age-matched healthy controls.