Philosophy of technology
A. Feenberg, Jairo D Carvalho
This course will introduce philosophy of technology through literature, major texts in the Continental tradition, and recent approaches to technology studies in the social sciences. We will begin with Aldous Huxley’s famous novel Brave New World which lays out in narrative form the dystopian terms of 20th Century technology critique. The domination of man by machine is the theme. A dystopian logic of technology underlies the philosophical work of Heidegger and his student Marcuse. Marcuse’s theory, however, opens up the possibility of a radical transformation of technology. With Habermas we have a very different attempt to come to terms with the existing technology by limiting its reach. The contributions of recent science and technology studies are represented here by articles by Pinch and Bijker and Latour, and a collection of case studies by Collins and Pinch. The constructivist approach is non-determinist and focuses on empirical study of cases. We will then read my recent book which draws on all these sources to present an approach I call “critical constructivism.” We will conclude with the contents of a special section of the Information Society Journal in which several authors apply critical constructivism to the study of information technology.
On-the-Fly Sleep Scoring Algorithm with Heart Rate, RR Intervals and Accelerometer as Input
Michele Guagnano, Sara Groppo, Luigi Pugliese
et al.
In many applications, recognizing the depth of sleep (e.g., light, deep, REM sleep) while the subject is sleeping enables innovative features. For instance, in SAE Level 4 autonomous driving, a driver may need to takeover the vehicle control in case the autopilot is exiting its operational design domain. Depending on the depth of the sleep, the subject may need time to takeover effectively; hence, it is particularly relevant to know in which sleep stage the subject is (e.g., light sleep, deep sleep, and REM sleep), and possibly initiate actions to prevent the subject to remain in those sleep stages that lead to longer takeover time. Sleep stage classification can be achieved through an on-the-fly algorithm, which generates output in response to each input portion without knowledge of future inputs, unlike an off-Line algorithm that provides output just after receiving the entire input sequence. Various studies have analyzed algorithms or devices that identify sleep stages during the night; however, these typically require electroencephalography (EEG), which is obtrusive, or specialized devices. This study describes the development of an on-the-fly sleep-scoring algorithm using Heart Rate (HR), RR intervals, which is the distance between two consecutive heartbeats, and accelerometer data from a smartwatch, widespread, non-invasive, and affordable but accurate device. The subjects involved in our study wore a commercial off-the-shelf wearable device during a full night’s sleep, and were also monitored using a reference medical device to establish the ground truth by means of a full polysomnography (PSG) analysis. The on-the-fly sleep scoring algorithm based on smartwatch data was tested against PSG-based scoring, achieving 88.46% accuracy, 91.42% precision, and 93.52% sensitivity in sleep–wake identification. Deep sleep was correctly identified 69.38% of times, light sleep in 50.62%, REM sleep 62.02% and wakefulness 73.48% of times.