## 4 Discussion  
The findings derived from the implementation of the initial method for compensating for leg length discrepancy (LLD) employing computer vision integrated into a smartphone, with a transtibial prosthesis serving as the effector, substantiate the viability of the adopted approach in restoring the user's body symmetry. The method's applicability extends to two distinct patient populations: those who have undergone unilateral amputation and are initially using a temporary transtibial prosthesis, and those in the growth phase. Figure 17: Evolution of the EPM acceleration along the Y-axis during a programmed stroke (advance and retreat). Figure 18: Evolution of the displacement speed of P1 during a programmed stroke (advance and retreat). In comparison with conventional LLD assessment methods that depend on medical imaging (most often X-rays or ultrasound) [8; 9; 16], functional evaluations (e.g., plantar analysis using baropodometric platforms) performed by specialists [11; 13], or automated assessments utilizing a dedicated Master-Slave system [24], our proposed solution is based on a rapid, non-invasive method with a brief patient preparation time. It is both financially and technically accessible, as nearly everyone today owns a smartphone. These considerations ensure a high potential for at-home implementation of the developed and tested method. The primary advantage of this approach lies in its use of a common device--a smartphone--thereby eliminating the need for expensive equipment. It has been demonstrated by preceding studies that contemporary clinical methodologies are accurate. Nevertheless, these methodologies necessitate specialized equipment and access to qualified medical personnel. Moreover, extant LLD compensation solutions are passive, lacking automation, and requiring regular manual adjustments [25; 26; 27; 28; 29; 30; 31; 32; 33]. For instance, certain prosthetic rods are engineered to permit length modification with an extension of up to 50 mm, in 1 mm increments, secured with a counter nut [31]. Another variant of an artificial lower limb enables length adjustment from 12.7 cm to 20.3 cm [28]. Additionally, Ottobock offers an adjustable prosthetic rod that allows for manual modification of prosthesis length [29]. As documented in the scientific literature, the extant solutions offer a range of length adjustment capabilities. However, these devices do not incorporate automated or semi-automated adjustment methods and necessitate the presence of a medical specialist to perform the modifications. Despite the substantial advancements in lower limb prostheses in recent years, from conventional systems to intelligent prosthetic systems managed by artificial intelligence (AI), allowing adaptability to various user interactions or environments, such as joint control through AI algorithms [40] or prostheses that can interpret electromyographic (EMG) signals from the patient to facilitate control during gait phases [41], AI remains underutilized for control of lower limb prostheses. However, the extant literature mentions applications of machine learning for fall detection in amputees and for managing the temperature within the prosthetic socket [42]. Recently, advanced methods for human gait pattern recognition have been proposed, with applications in sports and clinical contexts [43]. However, such complex systems do not provide a dedicated technical solution for correcting LLD in children with unilateral transtibial amputation, where a practical, adaptable, and accessible approach is required. Furthermore, a review of the specialized literature reveals a paucity of reports on lower limb prostheses that exhibit the capacity for automatic adaptation to the amputee's body conformation. This approach, in comparison to the previously described solutions, is characterized by its simplicity, feasibility at low cost, and the ability to maintain body symmetry without necessitating frequent visits to the prostheticist.