Feedback control of arm movements using Neuro-Muscular Electrical Stimulation (NMES) combined with a lockable, passive exoskeleton for gravity compensation.

Within the European project MUNDUS, an assistive framework was developed for the support of arm and hand functions during daily life activities in severely impaired people. This contribution aims at designing a feedback control system for Neuro-Muscular Electrical Stimulation (NMES) to enable reaching functions in people with no residual voluntary control of the arm and shoulder due to high level spinal cord injury. NMES is applied to the deltoids and the biceps muscles and integrated with a three degrees of freedom (DoFs) passive exoskeleton, which partially compensates gravitational forces and allows to lock each DOE The user is able to choose the target hand position and to trigger actions using an eyetracker system. The target position is selected by using the eyetracker and determined by a marker-based tracking system using Microsoft Kinect. A central controller, i.e., a finite state machine, issues a sequence of basic movement commands to the real-time arm controller. The NMES control algorithm sequentially controls each joint angle while locking the other DoFs. Daily activities, such as drinking, brushing hair, pushing an alarm button, etc., can be supported by the system. The robust and easily tunable control approach was evaluated with five healthy subjects during a drinking task. Subjects were asked to remain passive and to allow NMES to induce the movements. In all of them, the controller was able to perform the task, and a mean hand positioning error of less than five centimeters was achieved. The average total time duration for moving the hand from a rest position to a drinking cup, for moving the cup to the mouth and back, and for finally returning the arm to the rest position was 71 s

Inferencing into the void: problems with implicit populations Comments on `Empirical software engineering experts on the use of students and professionals in experiments'

I welcome the contribution from Falessi et al. [1] hereafter referred to as F++ , and the ensuing debate. Experimentation is an important tool within empirical software engineering, so how we select participants is clearly a relevant question. Moreover as F++ point out, the question is considerably more nuanced than the simple dichotomy it might appear to be at first sight. This commentary is structured as follows. In Section 2 I briefly summarise the arguments of F++ and comment on their approach. Next, in Section 3, I take a step back to consider the nature of representativeness in inferential arguments and the need for careful definition. Then I give three examples of using different types of participant to consider impact. I conclude by arguing, largely in agreement with F++, that the question of whether student participants are representative or not depends on the target population. However, we need to give careful consideration to defining that population and, in particular, not to overlook the representativeness of tasks and environment. This is facilitated by explicit description of the target populations.Comment: This is a commentary on the article in Empirical Software Engineering entitled `Empirical software engineering experts on the use of students and professionals in experiments' by Falessi et al. It will form part of an accepted an article for EMSE (Dec 2018) comprising commentary and a response from the original author

The Integrated Corporate Information Network iCoIN: A Comprehensive, Web-Based Experience Factory

During execution of projects, Software Organizations gain experience in their business. However, timely, effective and efficient reuse of this experience in subsequent projects is often problematic. The concept of an Experience Factory (EF), an infrastructure for continuous learning from experience, was designed to tackle these problems in the software development business. To install such an EF at the Fraunhofer Institute for Experimental Software Engineering (IESE), the knowledge management initiative Corporate Information Network (CoIN) was started in 1999. This paper presents the vision for CoIN and the practical experiences gained in its business process modeling part

Experiences and methods from integrating evidence-based software engineering into education

In today's software development organizations, methods and tools are employed that frequently lack sufficient evidence regarding their suitability, limits, qualities, costs, and associated risks. For example, in Communications of the ACM (Communications of the ACM May 2004/Vol. 47, No. 5) Robert L. Glass, taking the standpoint of practitioners, asks for help from research: "Here's a message from software practitioners to software researchers: We (practitioners) need your help. We need some better advice on how and when to use methodologies". Therefore, he demands: - a taxonomy of available methodologies, based upon their strengths and weaknesses; - a taxonomy of the spectrum of problem domains, in terms of what practitioners need; - a mapping of the first taxonomy to the second (or the second to the first). The evidence-based Software Engineering Paradigm promises to solve parts of these issues by providing a framework for goal-oriented research leading to a common body of knowledge and, based on that, comprehensive problemoriented decision support regarding SE technology selection

Towards an adaptive improvement management framework (Position Paper)

Software process improvement (SPI) knowledge is often brought into organizations from outside, for example, by external experts. The idea of this work is to build up an improvement management framework for supporting managers in their work; finding improvement potentials and making strategic decisions about the implementation of improvement measures. The adaptation of the information delivery to the manager's needs and skills is one aspect of the framework and is solved by employing case-based reasoning. Additionally, we use an organizational model that is put forward as the basis for choosing the improvement action that fits most appropriately within the context and is most promising with respect to the organizational goals

Eliciting requirements for a adaptive decision support system through structured user interviews

Eliciting user requirements at an early stage of software development can safe development time and effort. However, identify requirements for adaptivity, such as inter-individual differences in needs or preferences is not trivial. In this paper we revisit results reported in a previous paper from a methodological point of view. Using an example, we argue that scenarios in combination with structured interviews are not able to adequately identify adaptivity requirements due to reasons inherent to the method, such as the users' trust and their ability to anticipate system funtionality. We suggest that more implicit methods must be used at early development phases to obtain unbiased results

Reporting Experiments in Software Engineering

Background: One major problem for integrating study results into a common body of knowledge is the heterogeneity of reporting styles: (1) It is difficult to locate relevant information and (2) important information is often missing. Objective: A checklist for reporting results from controlled experiments is expected to support a systematic, standardized presentation of empirical research, thus improving reporting in order to support readers in (1) finding the information they are looking for, (2) understanding how an experiment is conducted, and (3) assessing the validity of its results. Method: The checklist for reporting is based on (1) a survey of the most prominent published proposals for reporting guidelines in software engineering and (2) an iterative development incorporating feedback from members of the research community. Result: This paper presents a unification of a set of guidelines for reporting experiments in software engineering. Limitation: The checklist has not been evaluated broadly, yet. Conclusion: The resulting checklist provides detailed guidance on the expected content of the sections and subsections for reporting a specific type of empirical studies, i.e., experiments (controlled experiments and quasi-experiments). 1

Innovative Man Machine Interfaces and Solutions to Support Totally Blind People

In this paper we present innovative MMI solutions to support social inclusion of totally blind people. We propose to use dedicated harness and mobile devices (e.g. smartphones) to support Instrumental Activities of Daily Living (IADL)