Shorter hospitalisation is a strategy that healthcare services have adopted to solve the problem of high demand for hospitalisation under conditions of insufficient resources. Early discharge demands patients to self-manage their recovery at home, while recovery is still perceived a clinician’s responsibility. This often results in an extra emotional and psychological load on patients. Due to the lack of measurements to include the emotional and psychological state of a patient in the home context, clinicians often oversee these issues in the evaluation of patients' recovery during hospital visits. This paper outlines the development of esther 1.0, an electronic tool based on Experience Sampling Method, that encourages senior patients of Total Hip Replacement (THR) to self-report their experiences related to their perceived physical and emotional state during the first weeks of recovery at home. This paper describes the emotional assessment of a THR patient and reflects on the value of this knowledge to develop professional support beyond the clinical recovery. @en

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.@en

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.@en

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.@en

Data collection and reflection are considered an integrated process in Personal Informatics to help users take action towards changing behaviour. Facilitating the collection and visualization of large data sets has been a major technical challenge to guarantee meaningful and effortless information to users. However this focus results in a passive involvement of users in these stages, creating distance between the user and their data, thus hindering proper understanding of people’s current behaviours. Designing for active participation may aid users in forming a closer bond to data. Going beyond the support of visualization of performance data, this paper introduces ESTHER 1.3 as an approach to facilitate active mini cycles of self-reflection (mCR) by means of in-situ self-reporting mechanisms. ESTHER 1.3 is presented as an implementation of this mini cycles in the context of physical activity and knowledge workers. A field study evaluation of 23 days with 5 users shows the opportunities of the mini cycles to engage people in deeper reflection and to support them to perform better-informed actions, as well as the challenges in the implementation of mCR elements for a specific context.@en

Data collection and reflection are considered an integrated process in Personal Informatics to help users take action towards changing behaviour. Facilitating the collection and visualization of large data sets has been a major technical challenge to guarantee meaningful and effortless information to users. However this focus results in a passive involvement of users in these stages, creating distance between the user and their data, thus hindering proper understanding of people’s current behaviours. Designing for active participation may aid users in forming a closer bond to data. Going beyond the support of visualization of performance data, this paper introduces ESTHER 1.3 as an approach to facilitate active mini cycles of self-reflection (mCR) by means of in-situ self-reporting mechanisms. ESTHER 1.3 is presented as an implementation of this mini cycles in the context of physical activity and knowledge workers. A field study evaluation of 23 days with 5 users shows the opportunities of the mini cycles to engage people in deeper reflection and to support them to perform better-informed actions, as well as the challenges in the implementation of mCR elements for a specific context.@en