Mid Sweden University

Stroke is a globally increasing disease and speech and language deficiencies are common in stroke survivors. To facilitate medical caregivers in their professional work and to improve patients’ quality of life, technology can play an important role. However, the use and acceptance of technology are uncertain and more research is needed in this direction. This study evaluates the technology acceptance and adoption of an online speech and language assessment application. The evaluation-focused Design Science Research strategy was adopted for that purpose. Two physiotherapists, one occupational therapist and three speech therapists participated in the study. The Unified Theory of Acceptance and Use of Technology (UTAUT) was used as the theoretical base for interview questions formation and data analysis. The study findings show that the suggested application is useful and easyto use; however, it should be better synchronised with speech therapists’ daily work routines. The speech therapists stressed that the functionalities of the application should be designed in close collaboration with them, and it should be compatible with the already existing systems and services in place. Due to impairments after stroke, the patients have some specific preferences for software and hardware; such as a tablet with a touch pen is the preferred hardware. Additionally, the interface should have bigger text fonts and pictures, and highly contrastive colours in the graphics should be used for patients’convenience. The user’s privacy and security, the patient’s current health, and their previous knowledge and experience with technology were also found important determinants for the intention to use the given technology.

We see an explosive global labour demand in the Software Industry, and higher education institutions play a crucial role in supplying the industry with professionals with relevant education. Existing literature identifies a gap between what software engineering education teaches students and what the software industry demands. Using our open-sourced Job Market AnalyseR (JMAR) text-analysis tool, we compared keywords from higher education course syllabi and job posts to investigate the knowledge gap from a technology-focused departure point. We present a trend analysis of technology in job posts over the past six years in Sweden. We found that demand for cloud and automation technology such as Kubernetes and Docker is rising in job ads but not that much in higher education syllabi. The language used in higher education syllabi and job ads differs where the former emphasizes concepts and the latter technologies more heavily. We discuss possible remedies to bridge this mismatch to draw further conclusions in future work, including calibrating JMAR to other industry-relevant aspects, including soft skills, software concepts, or new demographics. 

Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals’ biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events. 

 The growing world population is facing challengessuch as increased chronic diseases and medical expenses.Integrate the latest modern technology into healthcare systemcan diminish these issues. Internet of medical things (IoMT) isthe vision to provide the better healthcare system. The IoMTcomprises of different sensor nodes connected together. TheIoMT system incorporated with medical devices (sensors) forgiven the healthcare facilities to the patient and physician canhave capability to monitor the patients very efficiently. Themain challenge for IoMT is the energy consumption, batterycharge consumption and limited battery lifetime in sensor basedmedical devices. During charging the charges that are stored inbattery and these charges are not fully utilized due to nonlinearity of discharging process. The short time period neededto restore these unused charges is referred as recovery effect. Analgorithm exploiting recovery effect to extend the batterylifetime that leads to low consumption of energy. This paperprovides the proposed adaptive Energy efficient (EEA)algorithm that adopts this effect for enhancing energyefficiency, battery lifetime and throughput. The results havebeen simulated on MATLAB by considering the Li-ion battery.The proposed adaptive Energy efficient (EEA) algorithm is alsocompared with other state of the art existing method named,BRLE. The Proposed algorithm increased the lifetime ofbattery, energy consumption and provides the improvedperformance as compared to BRLE algorithm. It consumes lowenergy and supports continuous connectivity of devices withoutany loss/ interruptions. 

Speech and language loss is the most common disease for stroke survivors. The process of relearning communication skills is difficult and a time taking process. Technology-enhanced systems (TES) can be useful in speech and language relearning, however, the acceptance and usability of TES for stroke patients have been a matter of concern and more research is needed in this area. This study is therefore aimed to explore the factors that might influence the acceptance of technology-enhanced speech and language relearning after stroke. A systematic literature review was conducted to determine the technology acceptance factors. To ensure the state of the art in the given field, 97 articles written from 2016 to April 2021 were retrieved with a search string aligned to the research question. After applying the exclusion criteria and quality assurance, 13 articles were selected for inclusion. An overview of selected articles, their chosen methodology, and main findings from the articles was presented in a pre-defined table. The results show that patients’ physical and cognitive condition, the intensity of relearning exercises, native language, the involvement of friends and family, technical assistance and training, selection of hardware and usability of the graphical interface are important factors for acceptance of TES. Stroke patients tend to use TES. Independent living, treatment in the home environment, and improved quality of life are the major motivations for use of TES. However, TES should be tailor-made and a user-centre approach should be adopted. Finally, proper education and training are essential not only for the patients but for the speech therapists and patients’ relatives and friends as well.

This position paper gives better insight about the role and importance of Internet of Medical Things (IoMT) for independent living and re-learning for older adults. Sensing Technologies are the paradigm shift for transforming conventional healthcare practices into the smart, and self-assisted activities, which are envisioned for today's medical world. Internet of Things (IoT) and IoMT are the interrelated technologies for promoting independent living and re-learning practices. In this paper, re-learning is defined as the process for adults to recover useful instrumental activities of daily living skills that have been lost after an impairment.

Stroke is a globally increasing disease and speech and language deficiencies are common in stroke survivors. To facilitate medical caregivers in their professional work and to improve patients’ quality of life, technology can play an important role.  However, the use and acceptance of technology are uncertain and more research is needed in this direction. This study evaluates the technology acceptance and adoption of an online speech and language assessment application. The evaluation focused Design Science Research strategy was adopted for that purpose. Two physiotherapists, one occupational therapist and three speech therapists participated in the study. The Unified Theory of Acceptance and Use of Technology (UTAUT) was used as the theoretical base for interview questions formation and data analysis. The study findings show that the suggested application is useful and easy to use; however, it should be more synchronised with speech therapists’ daily work routines. The speech therapists stressed that functionalities of the application should be designed in close collaboration with them, and it should be compatible with the already existing systems and services in place.  Due to the impairments after stroke, the patients have some specific preferences for software and hardware such as a tablet with touch pen is the preferred hardware. Additionally, the interface should have bigger text fonts and pictures, and highly contrastive colours in the graphics should be used for patients’ convenience. The user’s privacy and security, patient’s current health, and their previous knowledge and experience about technology were also found important determinants for intention to use the given technology. 

Post stroke rehabilitation is a global issue with increasing challenges today when the percentage of older adults is increasing. There is a need for new solutions to better assist stroke survivors' normally long way back to a good and independent life. The various post stroke impairments can be divided into the categories of cognitive, motoric and speech impairment, and the three also have their interrelations. This position paper has a focus on rehabilitation of stroke survivors' speech impairments, and the use of technology-enhanced systems to assist the speech relearning. The current reuse of language learning software for primary school students is doubtful, and should better be replaced by tailor-made and adaptable tools that fit the target group. Finally, the recommendation is a long-term strategy where some initial costs should fund the design, development and evaluation of new digital tools for speech relearning. This should be conducted in a collaboration between researchers, speech therapists, stroke patients. The approach should be iterative and user-centred, with both speech therapists and stroke patients as the end-users.

Speech and language relearning are challenging for stroke survivors as well as for medical caregiv-ers. After a stroke, a patient’s ability to read, write, speak, and listen is decreased to different degrees, which results in a compromised independent life and a decreased quality of life for the patient. Tech-nology enhances systems can play a vital role in this context. However, the available software are not specifically built for after the stroke patient’s needs. This paper is therefore aimed to gather require-ments for designing a tailor-made speech relearning software application for stroke survivors. A de-sign science approach was adopted, where different stakeholders such as medical caregivers and in-formation technology consultants were involved in the process. The well-informed and experienced participants in their fields highlighted some important requirements such as different types of inter-face for a patient than speech therapist with extra management functionality for speech therapists so that they can adjust the relearning exercises according to the patient’s needs. Software requirements vary from patient to patient where the intensity of speech and language impairments, general medical condition of the patient, age, prior experience, and knowledge about the information of the patient and social setup of the patient plays an important role. Since stroke is most common in adults and adults learn differently than children, adult learning theory might help understand the patients' needs. There-fore, adult learning principles were involved in the requirement analysis process. The established re-quirements will be used for the development of speech and language relearning software.

Stroke is a common and severe disease that can be found in all regions across the globe, and not onlyamong older adults. Result of a stroke can be death, or a variety of disabilities caused by impairments indifferent brain functions. This chapter discusses technology enhanced stroke rehabilitation from a threefoldview of cognitive, motoric and speech rehabilitation. The important research question was: Whatwould be the requirements for technology-enhanced stroke rehabilitation in the areas of cognitive, motoricand speech rehabilitation? The study was carried out with a requirement-focused Design Science approachcollecting data with semi-structured interviews. Informants were selected in a purposive sampling choosingprofessionals with valuable knowledge and skills in stroke rehabilitation. The findings in this study havegenerated useful general requirements for a future implementation and testing of technology enhancedstroke rehabilitation. Within each of the three rehabilitation categories cognitive, motoric and speech, thereseems to be potential for successful use of technology enhanced services. This development ofrehabilitation services must follow the fundamental principle for all forms of stroke rehabilitation: eachpatient needs a personalised treatment. However, in all three rehabilitation categories, there is a need todefine more specific requirements based on feedback from stroke patients testing the rehabilitationservices.