Recent years have witnessed a growing interest in lower limb exoskeletons (LLEs) designed to restore walking capabilities for individuals with motor impairments. Currently, the research community faces the challenge of enhancing the translational impact of this technology by proposing solutions to move from laboratory and clinical settings to daily life scenarios. In this context, the topic of environment-adaptive gait planning (EAGP) is of particular interest and importance. EAGP refers to the ability of LLEs to assist in walking on various terrains (e.g., stairs and slopes) and under different conditions (e.g., the presence of obstacles and uneven ground) by adapting their behavior according to the context. However, the proliferation of publications has led to a general lack of convergence within the scientific community toward a common definition of this research topic. Therefore, this article proposes a review of EAGP for LLEs. The objectives are to: 1) provide a clear definition of EAGP, highlighting its main components and characteristics; 2) propose a taxonomy for classifying the most frequently used techniques in the state of the art; and 3) conduct a comprehensive survey of the literature, situating each article within the proposed taxonomy. In addition, we identify the most urgent future challenges toward the development of more autonomous LLEs.

MOTIVATION: Determining the relative contributions of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here, we present Annotation Informed-MiXeR, a likelihood-based method for estimating the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. RESULTS: Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes. Specifically, the exons of protein-coding genes harbor more than 90% of variants influencing type 2 diabetes and inflammatory bowel disease, making them good candidates for whole-exome studies. In contrast, <10% of the causal variants for schizophrenia, bipolar disorder and attention-deficit/hyperactivity disorder are located in protein-coding exons, indicating a more substantial role of regulatory mechanisms in the pathogenesis of these disorders. AVAILABILITY AND IMPLEMENTATION: The software is available at: https://github.com/precimed/mixer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.