Ilkka Korhonen

Bio: Ilkka Korhonen is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Remifentanil & Health care. The author has an hindex of 38, co-authored 155 publications receiving 6319 citations. Previous affiliations of Ilkka Korhonen include University of Tampere & Lappeenranta University of Technology.

Activity classification using realistic data from wearable sensors

Abstract: Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network

Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

Abstract: Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.

Health monitoring in the home of the future

Abstract: We discuss health monitoring as a potential application field for wearable sensors. We present some usage models for health monitoring and discuss the technical requirements for the health-monitoring system based on wearable and ambient sensors, which measure health-related data in daily environments of the users or patients. The presentation is by no means complete, but it aims to give an idea of the system-level issues to be considered for real applications. The technology in this area is rapidly developing, and without doubt we will evidence emergence of these applications in the coming years in the market.

Assessment of surgical stress during general anaesthesia

Abstract: Background Inadequate analgesia during general anaesthesia may present as undesirable haemodynamic responses. No objective measures of the adequacy of analgesia exist. We aimed at developing a simple numerical measure of the level of surgical stress in an anaesthetized patient. Methods Sixty and 12 female patients were included in the development and validation data sets, respectively. All patients had elective surgery with propofol–remifentanil target controlled anaesthesia. Finger photoplethysmography and electrocardiography waveforms were recorded throughout anaesthesia and various waveform parameters were extracted off-line. Total surgical stress (TSS) for a patient was estimated based on stimulus intensity and remifentanil concentration. The surgical stress index (SSI) was developed to correlate with the TSS estimate in the development data set. The performance of SSI was validated within the validation data set during and before surgery, especially at skin incision and during changes of the predicted remifentanil effect-site concentration. Results SSI was computed as a combination of normalized heart beat interval (HBInorm) and plethysmographic pulse wave amplitude (PPGAnorm): SSI=100–(0.7*PPGAnorm+0.3*HBInorm). SSI increased at skin incision and stayed higher during surgery than before surgery; SSI responded to remifentanil concentration changes and was higher at the lower concentrations of remifentanil. Conclusions SSI reacts to surgical nociceptive stimuli and analgesic drug concentration changes during propofol–remifentanil anaesthesia. Further validation studies of SSI are needed to elucidate its usefulness during other anaesthetic and surgical conditions.

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.

Abstract: Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static “snapshots” of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing “gold standard” measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a “knowledge commons,” which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Clinical Practice Guidelines for the Management of Pain, Agitation, and Delirium in Adult Patients in the Intensive Care Unit

Abstract: Objective:To revise the “Clinical Practice Guidelines for the Sustained Use of Sedatives and Analgesics in the Critically Ill Adult” published in Critical Care Medicine in 2002.Methods:The American College of Critical Care Medicine assembled a 20-person, multidisciplinary, multi-institutional task f

Activity recognition using cell phone accelerometers

Abstract: Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

A Survey on Human Activity Recognition using Wearable Sensors

Abstract: Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU

Abstract: Objective:To update and expand the 2013 Clinical Practice Guidelines for the Management of Pain, Agitation, and Delirium in Adult Patients in the ICU.Design:Thirty-two international experts, four methodologists, and four critical illness survivors met virtually at least monthly. All section groups g