TY - CHAP A1 - Seepold, Ralf A1 - Dermati, Christoph A1 - Kostka, Artur A1 - Pfeil, Lars A1 - Lange, Ralf A1 - Hermann, Matthias A1 - Martinez, Benedikt T1 - Analyzing environmental conditions and vital signs to increase healthy living T2 - Mobile Networks for Biometric Data Analysis (Lecture notes in electrical engineering ; Vol. 392) N2 - Present demographic change and a growing population of elderly people leads to new medical needs. Meeting these with state of the art technology is as a consequence a rapidly growing market. So this work is aimed at taking modern concepts of mobile and sensor technology and putting them in a medical context. By measuring a user’s vital signs on sensors which are processed on a Android smartphone, the target system is able to determine the current health state of the user and to visualize gathered information. The system also includes a weather forecasting functionality, which alerts the user on possibly dangerous future meteorological events. All information are collected centrally and distributed to users based on their location. Further, the system can correlate the client-side measurement of vital signs with a server-side weather history. This enables personalized forecasting for each user individually. Finally, a portable and affordable application was developed that continuously monitors the health status by many vital sensors, all united on a common smartphone. Y1 - 2016 SN - 978-3-319-39698-9 SN - 978-3-319-39700-9 U6 - http://dx.doi.org/10.1007/978-3-319-39700-9_3 SP - 27 EP - 39 PB - Springer CY - Cham ER - TY - CHAP A1 - Rajab, Mohammed A1 - Seepold, Ralf T1 - High performance dynamic threshold calibration for RR interval detection in a QRS complex using a parallel programing T2 - Mobile Networks for Biometric Data Analysis (Lecture notes in electrical engineering ; Vol. 392) N2 - These days computer analysis of ECG (Electrocardiograms) signals is common. There are many real-time QRS recognition algorithms; one of these algorithms is Pan-Tompkins Algorithm. Which the Pan-Tompkins Algorithm can detect QRS complexes of ECG signals. The proposed algorithm is analysed the data stream of the heartbeat based on the digital analysis of the amplitude, the bandwidth, and the slope. In addition to that, the stress algorithm compares whether the current heartbeat is similar or different to the last heartbeat after detecting the ECG signals. This algorithm determines the stress detection for the patient on the real-time. In order to implement the new algorithm with higher performance, the parallel programming language CUDA is used. The algorithm determines stress at the same time by determining the RR interval. The algorithm uses a different function as beat detector and a beat classifier of stress. Y1 - 2016 SN - 978-3-319-39698-9 SN - 978-3-319-39700-9 U6 - http://dx.doi.org/10.1007/978-3-319-39700-9_16 SP - 205 EP - 213 PB - Springer CY - Cham ER - TY - CHAP A1 - Seepold, Ralf T1 - Personalisierte Gesundheitsdienstleistung in assistiven privaten Umgebungen BT - Fachvortrag T2 - Fachtagung "Smart Day 2018", Veranstalter: Smart Home & Living Baden-Württemberg e.V., 7. Nov. 2018, Villingen-Schwenningen Y1 - 2018 ER - TY - CHAP A1 - Scherz, Wilhelm Daniel A1 - Soria Morillo, Luis Miguel A1 - Seepold, Ralf T1 - Biological data tracing and pattern recognition in real-time T2 - XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016, March 31st-April 2nd 2016, Paphos, Cyprus (IFMBE Proceedings ; Vol. 57) N2 - Stress is a recognized as a predominant disease with growing costs of treatment. The approach presented here is aimed to detect stress using a light weighted, mobile, cheap and easy to use system. The result shows that stress can be detected even in case a person’s natural bio vital data is out of the main range. The system enables storage of measured data, while maintaining communication channels of online and post-processing. Y1 - 2016 SN - 978-3-319-32703-7 SN - 978-3-319-32701-3 U6 - http://dx.doi.org/10.1007/978-3-319-32703-7_219 SP - 1119 EP - 1124 PB - Springer CY - Cham ER - TY - CHAP A1 - Scherz, Wilhelm Daniel A1 - Ortega, Juan Antonio A1 - Seepold, Ralf T1 - Towards emotion pattern extraction with the help of stress detection techniques in order to enable a healthy life T2 - JARCA 2015; ARCA XVII Conference on Qualitative Systems and Applications in Diagnosis, Robotics, Ambient Intelligence and Smart Cities, Vinaros, Spain, 23 to the 27 June 2015 Y1 - 2015 UR - http://madeirasic.us.es/jarca15/?page_id=25&lang=en SN - 978-84-608-5599-6 SP - 39 EP - 44 ER - TY - CHAP A1 - Scherz, Wilhelm Daniel A1 - Seepold, Ralf T1 - Detection of heart rate characteristics as an approach to distinguish between mental stress and physical activity T2 - Proceedings of the 12th Russian-German Conference on biomedical engineering, Vladimir State University Y1 - 2016 UR - https://elibrary.ru/item.asp?id=29081572 SN - 978-5-905527-12-8 SP - 69 EP - 72 ER - TY - CHAP A1 - Velicu, Oana Ramona A1 - Martínez Madrid, Natividad A1 - Seepold, Ralf T1 - Experimental sleep phases monitoring T2 - IEEE EMBS international conference on biomedical and health informatics (BHI) N2 - Nowadays there is a rich diversity of sleep monitoring systems available on the market. They promise to offer information about sleep quality of the user by recording a limited number of vital signals, mainly heart rate and body movement. Typically, fitness trackers, smart watches, smart shirts, smartphone applications or patches do not provide access to the raw sensor data. Moreover, the sleep classification algorithm and the agreement ratio with the gold standard, polysomnography (PSG) are not disclosed. Some commercial systems record and store the data on the wearable device, but the user needs to transfer and import it into specialised software applications or return it to the doctor, for clinical evaluation of the data set. Thus an immediate feedback mechanism or the possibility of remote control and supervision are lacking. Furthermore, many such systems only distinguish between sleep and wake states, or between wake, light sleep and deep sleep. It is not always clear how these stages are mapped to the four known sleep stages: REM, NREM1, NREM2, NREM3-4. [1] The goal of this research is to find a reduced complexity method to process a minimum number of bio vital signals, while providing accurate sleep classification results. The model we propose offers remote control and real time supervision capabilities, by using Internet of Things (IoT) technology. This paper focuses on the data processing method and the sleep classification logic. The body sensor network representing our data acquisition system will be described in a separate publication. Our solution showed promising results and a good potential to overcome the limitations of existing products. Further improvements will be made and subjects with different age and health conditions will be tested. KW - Sleep KW - Body sensor networks KW - Data acquisition KW - Electrocardiography KW - Electroencephalography Y1 - 2016 UR - https://ieeexplore.ieee.org/document/7455976 SN - 978-1-5090-2455-1 N1 - Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich. SP - 625 EP - 628 ER - TY - CHAP A1 - Gaiduk, Maksym A1 - Seepold, Ralf A1 - Orcioni, Simone A1 - Conti, Massimo A1 - Martínez Madrid, Natividad T1 - Non-invasive sleep analysis with intelligent sensors T2 - Smart-Future-Living-Bodensee : Proceedings of International Workshop, 24. November 2017, Hochschule Konstanz N2 - Sleep study can be used for detection of sleep quality and in general bed behaviors. These results can helpful for regulating sleep and recognizing different sleeping disorders of human. In comparison to the leading standard measuring system, which is Polysomnography (PSG), the system proposed in this work is a non-invasive sleep monitoring device. For continuous analysis or home use, the PSG or wearable Actigraphy devices tends to be uncomfortable. Besides, these methods not only decrease practicality due to the process of having to put them on, but they are also very expensive. The system proposed in this paper classifies respiration and body movement with only one type of sensor and also in a noninvasive way. The sensor used is a pressure sensor. This sensor is low cost and can be used for commercial proposes. The system was tested by carrying out an experiment that recorded the sleep process of a subject. These recordings showed excellent results in the classification of breathing rate and body movements. KW - Sleep study KW - Pressure sensor KW - Sleep KW - Signal processing Y1 - 2018 SP - 23 EP - 26 ER - TY - CHAP A1 - Gaiduk, Maksym A1 - Seepold, Ralf A1 - Rodríguez de Trujillo, Eva T1 - Sleep position recognition in home environment T2 - Fachtagung "Smart Day 2018", Veranstalter: Smart Home & Living Baden-Württemberg e.V., 7. Nov. 2018, Villingen-Schwenningen N2 - This paper presents a bed system able to analyze a person’s movement, breathing and recognize the positions that the subject is lying on the bed during the night without any additional physical contact. The measurements are performed with sensors placed between the mattress and the bed-frame. An Intel Edison board was used as an endpoint that served as a communication node from the mesh network to external service. Two nodes and Intel Edison are attached to the bottom of the bed frame and they are connected to the sensors. First test results have indicated the potential of the proposed approach for the recognition of sleep positions with 83% of correct recognized positions. KW - Sleep study KW - Bio-vital data KW - Non-invasive sleep study KW - Sleep positions Y1 - 2018 UR - https://www.shl-bw.de/home/aktuelles/detail-neuigkeiten/news/detail/smart-day-zeigt-wirkung-1-fachtagung-in-villingen-schwenningen-mit-grosser-resonanz/ SP - 21 EP - 24 ER - TY - JOUR A1 - Rodríguez de Trujillo, Eva A1 - Seepold, Ralf A1 - Gaiduk, Maksym T1 - Position recognition algorithm using a two-stage pattern classification set applied in sleep tracking JF - Procedia Computer Science N2 - The overall goal of this work is to detect and analyze a person's movement, breathing and heart rate during sleep in a common bed overnight without any additional physical contact. The measurement is performed with the help of sensors placed between the mattress and the frame. A two-stage pattern classification algorithm based has been implemented that applies statistics analysis to recognize the position of patients. The system is implemented in a sensors-network, hosting several nodes and communication end-points to support quick and efficient classification. The overall tests show convincing results for the position recognition and a reasonable overlap in matching. KW - Movement detection KW - Sleep positions KW - Sleep study KW - Pattern recognition Y1 - 2018 U6 - http://dx.doi.org/10.1016/j.procs.2018.08.095 SN - 1877-0509 VL - 126 SP - 1819 EP - 1827 ER -