Exploiting variability in the design of genetic algorithms to generate telerehabilitation activities - Moya, Alejandro; Navarro, Elena; Jaén, Javier; López-Jaquero, Víctor; Capilla, Rafael (Applied soft computing, 2022)
The increasing number of people with impairments and the lack of specialists has led to a loss of efficiency to deliver proper treatments from National healthcare systems. In this light, telerehabilitation can play an important role as patients can perform certain therapies at home. Consequently, telerehabilitation systems must support delivering bespoke therapies to patients tailored to their deficits and preferences. However, creating bespoke telerehabilitation activities is a complex and time-consuming task because of the great assortment of deficits. To address this problem, we propose in this research work an automatic generation of such telerehabilitation activities aiming to both assist the specialist in designing and creating telerehabilitation activities that best fit each patient’s needs. Therefore, the main contributions of this paper are: (1) the exploitation of Feature Models (FM) to describe the variability in the telerehabilitation domain and to facilitate the communication among the stakeholders to accurately specify the patients’ deficits and the features of an association telerehabilitation activity. (2) The design and development of a genetic algorithm (GA) relying on the specified FM able to generate customized association telerehabilitation activities. The FM specified describes precisely the search problem so that the GA chromosomes can be easily identified. It also facilitates the discussion with the stakeholders during the design of the algorithm since its specification can be understood by non-experts in Computer Science. (3) The evaluation of the effectiveness and efficiency of the GA developed by using two sets of experiments: one for tuning the parameters of the GA and another to assess the effectiveness and efficiency of the algorithm while stressed under constraining conditions. (4) The integration of the proposal with a tool for telerehabilitation of people with Acquired Brain Injury (ABI). The proposal targets people with ABI because of the wide assortment of deficits they present, as well as the high impact ABI is having on society, being currently more common than breast cancer, spinal cord injury, HIV/AIDS and multiple sclerosis (MS) combined. •Creating rehabilitation activities is a challenging and time consuming task.•People with ABI suffer a wide diversity of deficits with different severity.•Generation of rehabilitation activities is approached as an optimization problem.•Feature models were proven useful to identify the genes for our genetic algorithm.•Genetic algorithm generates effectively and efficiently rehabilitation activities.