Informing the Design of Personal Informatics Technologies for Unpredictable Chronic Conditions

Personal informatics technologies, such as consumer fitness tracking devices, have an enormous potential to transform the self-management of chronic conditions. However, it is unclear how people living with relapsing and progressive illnesses experience personal informatics tools in everyday life: what values and challenges are associated with their use? This research informs the design of future health tracking technologies through an ethnographic design study of the use and experience of personal informatics tools in multiple sclerosis (MS) self-management. Initial findings suggest that future health tracking technologies should acknowledge people’s emotional wellbeing and foster flexible and mindful self-tracking, rather than focusing only on tracking primary

Self-Experimentation and the Value of Uncertainty

Self-tracking technologies have a great potential to transform the ways people understand and manage their personal health and wellbeing. However, studies on self-tracking suggest that people face challenges in gaining self-knowledge because of a lack of scientifically robust self-experimentation systems. In this position paper we focus attention on the value of uncertainty in self-experimentation by drawing on diagnostic tracking practices in multiple sclerosis (MS). In doing so, we illustrate the role of uncertainty and scientific thinking in self-tracking and managing the complex and unpredictable nature of the disease. Based on this understanding, we propose a set of design considerations to motivate discussion of the ways in which the design of future self-experimentation tools could spark scientific thinking and acknowledge uncertainty in everyday life

Reflections on 5 Years of Personal Informatics: Rising Concerns and Emerging Directions

The real world use and design of personal informatics has been increasingly explored in HCI research in the last five years. However, personal informatics research is still a young multidisciplinary area of concern facing unrecognised methodological differences and offering unarticulated design challenges. In this review, we analyse how personal informatics has been approached so far using the Grounded Theory Literature Review method. We identify a (1) psychologically, (2) phenomenologically, and (3) humanistically informed stream and provide guidance on the design of future personal informatics systems by mapping out rising concerns and emerging research directions

Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations

BACKGROUND: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer term control. METHODS: We introduce explainable machine learning to make predictions of hypoglycemia (270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. RESULTS: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. CONCLUSIONS: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user’s glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications

Model-based reinforcement learning for type 1 diabetes blood glucose control

In this paper we investigate the use of model-based reinforcement learning to assist people with Type 1 Diabetes with insulin dose decisions. The proposed architecture consists of multiple Echo State Networks to predict blood glucose levels combined with Model Predictive Controller for planning. Echo State Network is a version of recurrent neural networks which allows us to learn long term dependencies in the input of time series data in an online manner. Additionally, we address the quantification of uncertainty for a more robust control. Here, we used ensembles of Echo State Networks to capture model (epistemic) uncertainty. We evaluated the approach with the FDA-approved UVa/Padova Type 1 Diabetes simulator and compared the results against baseline algorithms such as Basal-Bolus controller and Deep Q-learning. The results suggest that the modelbased reinforcement learning algorithm can perform equally or better than the baseline algorithms for the majority of virtual Type 1 Diabetes person profiles tested. Copyright © 2020 for this paper by its authors

Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine Learning

Understanding artificial intelligence (AI) and machine learning (ML) approaches is becoming increasingly important for people with a wide range of professional backgrounds. However, it is unclear how ML concepts can be effectively explained as part of human-centred and multidisciplinary design processes. We provide a qualitative account of how AI researchers explained and non-experts perceived ML concepts as part of a co-design project that aimed to inform the design of ML applications for diabetes self-care. We identify benefits and challenges of explaining ML concepts with analogical narratives, information visualisations, and publicly available videos. Co-design participants reported not only gaining an improved understanding of ML concepts but also highlighted challenges of understanding ML explanations, including misalignments between scientific models and their lived self-care experiences and individual information needs. We frame our findings through the lens of Stars and Griesemer’s concept of boundary objects to discuss how the presentation of user-centred ML explanations could strike a balance between being plastic and robust enough to support design objectives and people’s individual information needs

Playful, Curious, Creative, Equitable : Exploring Opportunities for AI Technologies with Older Adults

There has recently been much discussion around OpenAI, Generative AI, use of chatbots and the use of other immersive technologies in the mainstream. These developments have much to offer to older adults in terms of playful, accessible and creative ways to engage with technology in everyday life. In this workshop, we are interested in developing a research agenda for HCI research with older adults to explore, enjoy, build new and extend existing interactions with such technologies. What are the possibilities they offer simply for introducing creativity, playfulness, enjoyment and ‘fun’ for older adults in everyday life? Or are there other goals that older adults want to achieve using them, such as new ways of socially engaging with their grandchildren, developing hobbies and knowledge, or simply making their lives easier? Can these tools empower older adults to explore various interaction modalities to help them achieve their goals? Finally, what are the new ways that these tools can be used to engage with older adults in the research and design of new emerging technologies? In this workshop, we will aim to generate discussion, develop a community and a roadmap for older adults’ use of technology that is playful, curious, creative and equitable. We will focus on five themes for the role of such technologies: (i) for enabling expression and creativity, (ii) as a catalyst for experience and action, (iii) for enabling reflection and awareness, (iv) for communication and (v) supporting the design process for (re) inventing new products and avenues for use. This workshop will feature co-creation and exploration of research methods and technologies, with panel and multidisciplinary discussions bringing together researchers who are interested in designing for and with older adults. We will explore new technology interactions including AI and immersive technologies within HCI; discussing methods, opportunities, and challenges in using these technologies and leveraging them for ideation, and form a multidisciplinary community for future synergies and collaborations

PENGATURAN INTENSITAS CAHAYA DALAM RUANGAN KERJA UNTUK MEMPERTINGGI AKTIVITAS DALAM BEKERJA DAN MENGURANGI CEDERA PADA MATA MENGGUNAKAN KONTROLER PID BERBASIS ARDUINO

Pengaturan  intensitas cahaya merupakan solusi dalam  upaya mencegah cedera pada mata dalam ruangan kerja. Dalam penelitian ini akan memberikan  solusi untuk membatasi cahaya yang masuk ke dalam plant. Sensor LDR(Light Dependent Resistor) berfungsi untuk menangkap cahaya yang masuk pada plant setelah di feedback oleh PWM dengan kontroler PID. Kontroler PID banyak digunakan di dunia industri dikarenakan memiliki keunggulan respon cepat, overshoot dan error kecil. Proses perancangan kontroler PID menggunakan metode pertama Ziegler Nichols dan didapatkan parameter PID yaitu Kp=4.83, Ki=67.08 dan Kd=0.087. Pengendalian dirancang agar intensitas cahaya pada plant sesuai dengan setpoint yaitu 250 lux. Hasil pengujian terhadap keseluruhan sistem diperoleh error steady state sebesar 1.26 %. Percoban saat plant diberikan gangguan sensor terhalang kertas putih membutuhkan recovery time 0.55 detik, sedangkan saat diberi gangguan cahaya tambahan(senter) membutuhkan recovery time selama 0.41 detik .Setelah mengalami gangguan kontroler PID mampu kembali menuju setpoint. Kata Kunci: Pengaturan intensitas cahaya, sensor LDR, Arduino Mega 2560, Kontroler PID, Dimmer, Ruangan kerja