Dimitri Kraft

Proceedings of the 14th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <14, 2021, Online>

ACM International Conference Proceedings Series (ICPS)

After a knee or hip surgery, a fast mobilization of the patient is highly recommended. The best therapy would be individual physiotherapy or guided walks with personal assistance, but unfortunately, this is very quite expensive and clinics are suffering from a lack of well-educated personnel. With RoRo, an autonomous walking frame (rollator), the patient receives walking support, reminders for exercises, and a measurement of the gait and walking parameters. For the navigation purposes of the autonomous rollator within the building of a clinic, a precise indoor navigation concept is needed that even works without a continuous internet connection. This paper describes the location and navigation concept with optical barcodes that are stuck at the top of each floor. The information of the barcodes is coded in the way, that they contain orientation features and are spanning a hierarchical structure.

Berlin, Technische Univ., Master Thesis, 2021

Der Blutdruck (BP) ist ein grundlegendes Zeichen, das wichtige Gesundheitsinformationen über Patienten liefert. Es ist sehr wichtig, den Blutdruck bei Patienten mit Bluthochdruck kontinuierlich zu überwachen, und der Blutdruck ist ein entscheidender Risikofaktor für eine Vielzahl von Herz-Kreislauf-Erkrankungen, die 2015 die häufigste Todesursache waren und laut der Weltgesundheitsorganisation (WHO) für 15 Millionen Todesfälle weltweit verantwortlich sind. Diese Arbeit konzentriert sich auf die Entwicklung eines tiefen neuronalen Netzwerks zur Schätzung der physiologischen Parameter Herzfrequenz (HR), systolischer Blutdruck (SBP) und diastolischer Blutdruck (DBP) unter Verwendung von Photo-Plethysmographie (PPG) Signalen. Wir analysierten die MIMIC-III-Datenbank, um die hochwertigen PPG-Signale und die entsprechenden arteriellen Blutdrucksignale für 100 Patienten bei 125 Hz zu extrahieren. Bei der MIMIC III-Datenbank handelt es sich um einen öffentlich zugänglichen größeren Datensatz, der Daten von Intensivpatienten mit kardiovaskulären Erkrankungen (CVDs) enthält. Die Länge der Daten variiert von Patient zu Patient je nach Dauer des Aufenthalts auf der Intensivstation. Die Signale in diesem Datensatz sind aufgrund von Sensorproblemen mit einigen Artefakten behaftet. Die Signale in diesem Datensatz wurden vorverarbeitet, um Artefakte wie physiologisch unwahrscheinliche Blutdruckwerte, Rauschen und Schwankungen in den Signalen zu entfernen. Der bereinigte Datensatz wurde nach dem Zufallsprinzip in 60 Prozent Trainingsmenge, 20 Prozent Validierungsmenge und 20 Prozent Testmenge aufgeteilt. Das vorgeschlagene Modell des tiefen neuronalen Netzes wurde unter Verwendung eines Faltungsneuronalen Netzes (CNN) und eines Modells mit langem Kurzzeitgedächtnis (LSTM) entwickelt, um drei Parameter zu schätzen, nämlich den diastolischen Blutdruck (DBP), den systolischen Blutdruck (SBP) und die Herzfrequenz (HR), wobei ein PPG-Signal als Eingabe für das System verwendet wurde. Für die Herzfrequenz (HR) prognostizierte das Modell einen mittleren absoluten Fehler (MAE) von 9; 379, einen RMSE von 13; 95 und einen R2-Wert von 0,47. Für den SBP schätzte das Modell den MAE-Wert auf 12; 46, den RMSE auf 16; 73 und den R2 auf 0,427. Für DBP schätzte das Modell den MAE auf 10; 84, den RMSE auf 11; 35 und den R2 auf 0,378. Die Ergebnisse zeigten eine gute Übereinstimmung im Vergleich zu den globalen Standards und der vorgeschlagene Rahmen kann für die BP-Schätzung verwendet werden.

Proceedings of the 14th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <14, 2021, Online>

Corporate health management is an important tool for preventing work-related absenteeism, increasing overall employee satisfaction and reducing the costs of absenteeism or presenteeism in the long term. Today, corporate health management is even more important because many employees work from home. Often, there is a lack of workspace or even a workplace that meets minimum occupational health and safety guidelines. Overwork, noise pollution, incorrect sitting posture and unstructured work breaks contribute negatively to the daily work routine, as does the general problem of separating work and leisure. Under these conditions, daily work is made even more difficult, which can lead to increased mental and physical stress. A concept for unobtrusive monitoring to increase long-term health, improve working conditions or at least to show the necessary adjustments to the new work situation can help to solve these problems. This paper presents a concept that shows how a simple webcam can be used to record essential vital signs during working hours, evaluate them using machine learning, and offer intervention recommendations based on these data to reduce psychological and physical stress. Work on continuous stress measurement and the challenges associated with it will be presented. This work serves as a starting point for the development of a camera-based tool for mental and physical stress measurement in theworkplace. Our approach demonstrates that the required parameters can be captured using a simple webcam and that interventions can be used to achieve long-term reductions in work-related mental and physical stress, provided that the proposed interventions are followed. The prototypical implementation shows that such a solution can work well in the workplace, but that data protection and technical limitations must be considered in the future in order to establish camera-based methods in the toolbox of workplace health management.

Rostock, Univ., Master Thesis, 2021

Die vorliegende Arbeit präsentiert ein System zur Vereinheitlichung von Data Sets aus heterogenen Quellen. Mittels Techniken des Ontology-Matchings erstellt dieses eine Ontologie der Beziehungen zwischen Data Sets, welche in einem Kreis-Layout visualisiert wird und bearbeitet werden kann. Anschließend benennt das System Variablen der Data Sets entsprechend der Ontologie um. Um die Eignung unterschiedlicher Matching-Techniken für die Erstellung der Ontologie zu untersuchen, werden mehrere Ansätze implementiert. Darunter sind Methoden auf Basis von Zeichenkettenvergleichen, Word-Embeddings mit GloVe und BERT und Techniken, die Instanzdaten berücksichtigen. Anhand einer aus realen Daten erstellten Ground-Truth werden diese hinsichtlich F1-Score, Precision, Recall und Average-Precision evaluiert. Alle Ansätze schneiden besser ab als ein Weighted Guessing Classifier und eignen sich daher grundlegend zur Unterstützung der Vereinheitlichung. Hinsichtlich des F1-Score und Recall schneidet eine Methode auf Basis erlernter GloVe-Embeddings am besten ab, ihr F1-Score von 0.387 und Recall von 0.319 lassen jedoch Raum für Verbesserungen. Eine Methode auf Basis von Zeichenkettenvergleichen liefert mit einem Wert von 0.358 das zweitbeste Ergebnis im Hinblick auf den F1-Score und mit einem Wert von 0.739 die beste Precision. Die Berücksichtigung von Instanzdaten verbessert nur bei einer von vier implementierten Methoden die Leistung hinsichtlich Average Precision. Dadurch liefert der Ansatz, welcher Zeichenkettenvergleiche mit der Berücksichtigung von Instanzdaten kombiniert, mit einem Wert von 0.391 die beste Average Precision aller verglichenen Techniken.

Technologies

A fall of an elderly person often leads to serious injuries or even death. Many falls occur in the home environment and remain unrecognized. Therefore, a reliable fall detection is absolutely necessary for a fast help. Wrist-worn accelerometer based fall detection systems are developed, but the accuracy and precision are not standardized, comparable, or sometimes even known. In this work, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly. Furthermore, we give an outlook for a convenient application and wrist device.

Proceedings of the 9th International Conference on Sensor Network

International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <15, 2020, Valetta, Malta>

Natural and artificial joints of a human body are emitting vibration and sound during the movement. The sound and vibration pattern of a joint is characteristic and changes due to damage, uneven tread wear, injuries, or other influences. Hence, the vibration and sound analysis enables an estimation of the joint condition. This kind of analysis, vibroarthrography (VAG), allows the analysis of diseases like arthritis or osteoporosis and might determine trauma, inflammation, or misalignment. The classification of the vibration and sound data is very challenging and needs a comprehensive annotated data base. Current existing data bases are very limited and insufficient for deep learning or artificial intelligent approaches. In this paper, we describe a new concept of the design of a vibroarthrography system using a sensor network. We discuss the possible improvements and we give an outlook for the future work and application fields of VAG.

2020 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings

International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020) <45, 2020, online>

Electrocardiograms constitute the key diagnostic tool for cardiologists. While their diagnostic value is yet unparalleled, electrode placement is prone to errors, and sticky electrodes pose a risk for skin irritations and may detach in long-term measurements. Heart.AI presents a fundamentally new approach, transforming motion-based seismocardiograms into electrocardiograms interpretable by cardiologists. Measurements are conducted simply by placing a sensor on the user’s chest. To generate the transformation model, we trained a convolutional autoencoder with the publicly available CEBS dataset. The transformed ECG strongly correlates with the ground truth (r=.94, p<.01), and important features (number of R-peaks, QRS-complex durations) are modeled realistically (Bland-Altman analyses, p>0.12). On a 5- point Likert scale, 15 cardiologists rated the morphological and rhythmological validity as high (4.63/5 and 4.8/5, respectively). Our electrodeless approach solves crucial problems of ECG measurements while being scalable, accessible and inexpensive. It contributes to telemedicine, especially in low-income and rural regions worldwide.

Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Corfu, Greece>

ACM International Conference Proceedings Series (ICPS)

Knees, hip, and other human joints generate noise and vibration while they move. The vibration and sound pattern is characteristic not only for the type of joint but also for the condition. The pattern vary due to abrasion, damage, injury, and other causes. Therefore, the vibration and sound analysis, also known as vibroarthrography (VAG), provides information and possible conclusions about the joint condition, age and health state. The analysis of the pattern is very sophisticated and complex and so approaches of machine learning techniques were applied before. In this paper, we are using convolutional neural networks for the analysis of vibroarthrographic signals and compare the results with already known machine learning techniques.

Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Corfu, Greece>

ACM International Conference Proceedings Series (ICPS)

With increasing age, elderly persons are falling more often. While a third of people over 65 years are falling once a year, hospitalized people over 80 years are falling multiple times per year. A reliable fall detection is absolutely necessary for a fast help. Therefore, wristworn accelerometer based fall detection systems are developed but the accuracy and precision is not standardized, comparable or sometimes even known. In this paper, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly.

iWOAR 2019

International Workshop on Sensor-based Activity Recognition (iWOAR) <6, 2019, Rostock, Germany>

ACM International Conference Proceedings Series

The knee is the largest joint in the human body. Unfortunately, some hips or knee joints suffer on inflammation, misalignment, degeneration, trauma as well as diseases like arthritis or osteoporosis. Modern medicine can measure the joint condition or, if the joint is worn out, even exchange the joint with an implant. Endoprosthetic implants are artificial devices that replaces a weak body part such as osteoarthritic knee or hip joints. The lifespan of joint endoprostheses are also limited and depend on several factors, and it varies for each patient. In most cases total knee or hip endoprostheses need to be replaced after approximately 15 to 20 years, but some implants need an exchange after a few years due to several causes. Current methods to examine the condition of joint endoprostheses and natural joints are X-ray, Computed tomography (CT) and Magnetic Resonance Imaging (MRI). In rare cases implant integrated sensors were used. The usage of these methods and the analysis of the assessed data require medical and data experts. However, a vague estimation of the joint condition can also be performed by external vibration and sound analysis of the endoprosthesis and natural joint during movements. This paper describes several approaches of external vibration and sound analysis as a survey

Rostock, Univ., Master Thesis, 2019