## 2 PICO-Inspired Impairment Analysis for Hand Exoskeletons  
The PICO model represents a method for formulating search questions and characterizing meta-analyses or clinical studies [18]. Several variants of the method have been proposed over the years, all slightly modifying the original version, which is grounded on four elements that designate the potential components of a clinical question: population/problem, intervention, comparison, and outcome. The first element (P) describes the main target of the study, identifying the characteristics of the population or the problem. In the current study, the investigated problem is the kind of impairment of the patient, as the hand exoskeleton end-user. The second component (I) defines the intervention (e.g., physical therapy, pharmacological treatment) under consideration for the specific population, whereas the third element (C) focuses on the comparison between the proposed intervention and possible alternatives (e.g., from placebo to surgery). Both of these aspects can offer interesting insights for the researcher: the analysis of the intervention can, for instance, highlight additional unexpected but relevant requirements for the design of new devices [19]. The comparison with competitors can instead provide precious hints for the development of a device, for example in the demonstration of equivalence, which represents a mandatory step of the clinical evaluation of a medical device for marketing in European countries [20]. Finally, the fourth element (O) of the PICO model defines the outcome considered relevant to assess the effectiveness of the intervention. These outcomes could be of various natures, from clinical assessments with instrumented measurement tools like dedicated sensors, to clinical scales, as well as economic factors (e.g., the number of accesses to the hospital or to medical examinations) or humanistic end-points, such as quality of life or patient satisfaction with the performed intervention. In our analysis, which focuses on device design and selection, outcomes play a very important role. In particular, outcomes that involve the measurement of specific hand functions can drive the decision to include certain sensors and/or tracking systems in the device during the design phase. For example, outcome measures that involve the use of goniometers could lead to sensors being integrated into the device for angular measurement. A correlation can also be found between outcomes and the main features of the exoskeleton. The strength outcome, which can be measured either with ergometers or with clinical scales, such as the Medical Research Council (MRC) scale, is indeed related to the functional feature of force. This leads to the following logic: the outcomes used to assess the effectiveness of a treatment are related to the features, and the more or less pronounced predisposition of an exoskeleton with respect to the features makes it more or less suitable for a given impairment. Table 1 collects the P and O elements of the PICO model for the current investigation, i.e., the possible impairments described in the literature for the end-user of a hand exoskeleton and the referring outcomes. Since the final aim of this analysis step is to enable the identification of the clinical requirements for mapping with the technical features, the I and C components of PICO are not reported in this first stage. Table 2 presents a first attempt at mapping between impairments and pathologies. Only the most common diseases for this context was evaluated in this phase with the main aim of providing an overview of the complex relation between diseases and derived impairments for the inexperienced observer. With this purpose, cancer was also included, although the great potential variability of this disease (e.g., in type and location) prevents a generally valid mapping with impairments.