Martin Jacobsson’s research while affiliated with KTH Royal Institute of Technology and other places

In modern hospitals, monitoring patients’ vital signs and other biomedical signals is standard practice. With the advent of data-driven healthcare, Internet of medical things, wearable technologies, and machine learning, we expect this to accelerate and to be used in new and promising ways, including early warning systems and precision diagnostics. Hence, we see an ever-increasing need for retrieving, storing, and managing the large amount of biomedical signal data generated. The popularity of standards, such as HL7 FHIR for interoperability and data transfer, have also resulted in their use as a data storage model, which is inefficient. This article raises concern about the inefficiency of using FHIR for storage of biomedical signals and instead highlights the possibility of a sustainable storage based on data compression. Most reported efforts have focused on ECG signals; however, many other typical biomedical signals are understudied. In this article, we are considering arterial blood pressure, photoplethysmography, and respiration. We focus on simple lossless compression with low implementation complexity, low compression delay, and good compression ratios suitable for wide adoption. Our results show that it is easy to obtain a compression ratio of 2.7:1 for arterial blood pressure, 2.9:1 for photoplethysmography, and 4.1:1 for respiration.

Video is the most used tool for sport performance analysis as it provides a common reference point for the coach and the athlete. The problem with video is that it is a subjective tool. To overcome this, motion capture systems can used to get an objective 3D model of a person’s posture and motion, but only in laboratory settings. Unfortunately, many activities, such as most outdoor sports, cannot be captured in a lab without compromising the activity. In this paper, we propose to use an aerial drone system equipped with depth cameras, AI-based marker-less motion capture software to perform automatic skeleton tracking and real-time sports performance analysis of athletes. We experiment with off-the-shelf drone systems, miniaturized depth cameras, and commercially available skeleton tracking software to build a system for analyzing sports-related performance of athletes in their real settings. To make this a fully working system, we have conducted a few initial experiments and identified many issues that still needs to be addressed.KeywordsQuadcopterDroneMotion captureSkeleton trackingDepth cameraSports performance analysis

The advent of Low-Power Wide Area Networks has enabled significant developments of the IoT ecosystem. Long range communication using low power is now feasible and offers connectivity to remote areas where cellular network is not available. Therefore, new application scenarios have emerged, such as smart cities, smart metering and more, which are attracting a lot of attention from both research and industry. Beside the aforementioned popular scenarios, Low-Power Wide Area Networks have started to be used in wearable systems scenarios as well. In this position paper, we pose some questions regarding the Human Computer Interaction aspects of Low-Power Wide Area Networks which will help them integrate in Ubiquitous Computing applications. We illustrate by a wearable system, which is based on an foot gesture interface, a Low-Power Wide Area Network, and an Neural Network classifier. The discussion is based on the state of art of foot interfaces and highlights open issues and challenges. CCS CONCEPTS • Computer systems organization → Embedded and cyber-physical systems; • Human-centered computing → Gestural input; Human computer interaction (HCI).

Low-Power Wide Area Networks have contributed in several parts of the Internet of Things ecosystem during the last years by enabling long range, robust and low power communication. Machine Learning for embedded systems has also assisted the advancement of the Internet of Things by identifying patterns and increasing the accuracy of predicting events and behaviours. At the same time, wearable and mobile systems are less obtrusive, consuming less energy and have more computing resources. In this paper we combine these three components and propose a low cost wearable system based on a regular shoe and off-the-shelf electronics which is able to recognize foot gestures and transmit messages over long range, in cases of emergency. The evaluation considers an application scenario where the user performs specific foot gestures to trigger the transmission of an emergency message, during other activities (e.g., walking). The proposed wearable system would benefit a user who is in danger and attempts to notify her/his emergency contacts in a discreet manner. Results show that the proposed system is able to identify the intended foot gestures with 98% accuracy.

Today, the programming of a complete wearable sensor system requires writing code in different programming languages for the different parts of the systems, such as the wearable sensor platform itself, the gateway, the back-end server, and the client app. In this paper, we propose to use WebAssembly, which is a simple but powerful virtual machine standard already supported by all major web browsers. We show that it is possible to implement a WebAssembly interpreter for embedded systems, such as the Texas Instruments CC2652R system-on-chip and this enables the same code to execute in all parts of the systems. In our proof-of-concept implementation, we use Bluetooth low energy, which means that smartphones can communicate with and program our device without the need for special hardware.

Low-Power Wide-Area Network (LPWAN) is an emerging technology in IoT which is utilized in use cases like smart cities and smart agriculture. LPWAN offers new features to wearable systems and therefore introduces new application scenarios. Long range communication is already used in application scenariosm such as elderly monitoring, localization, sportsm etc. We demonstrate how LPWAN wearables can operate by using energy-harvesting means and be battery-free. This preliminary study with real experiments illustrates how the LoRa radio is able to operate by a combination of solar and mechanical energy on a smart shoe prototype outdoors. The results indicate that the activity can influence the harvest rate.

The packet error rate of wireless links is known to increase with the length of packets. Yet, packet length is rarely taken into account in protocols and algorithms estimating the packet error rate. Still, it is an important factor that higher layer protocols need to be aware of. In this article, we systematically measure the relationship between packet length and packet error rate over a wide range of wireless links and technologies. On the basis of our measurements, we propose a simple empirical model that can capture this behavior. Using this model, multiple methods are proposed that can estimate the packet error rate for any packet length by sampling the link. We consider methods based on hello packets with controlled packet lengths as well as data packets, where the transmitted packet lengths cannot be controlled. We investigate the accuracy of the different estimation methods in various situations and show how they are able to predict the delivery ratio for different packet sizes. The packet error rate of wireless links is known to increase with the length of packets. In this article, we systematically measure the relationship between packet length and packet error rate over a wide range of wireless links and technologies. Based on the result of this, multiple methods are proposed that efficiently and accurately can estimate the packet error rate for any packet length by sampling the link.

Due to their popularity, large coverage areas, and diversity of radio technologies, Low Power Wide Area Networks (LPWAN) will experience high levels of cross-technology interference. In this work, we investigate the impact of LoRa interference on IEEE 802.15.4 (Wi-SUN) networks. Using hardware experiments , we characterize the interaction between these two very different radio technologies. In particular, we observe that the IEEE 802.15.4 Clear Channel Assessment (CCA) mechanism does not reliably detect interfering LoRa transmissions. We therefore propose an enhanced CCA mechanism based on a Multi-Layer Perceptron classifier and show that it significantly reduces the number of unsuccessful transmissions, while remaining compatible with the IEEE 802.15.4 standard.

We present some preliminary results on LoRaWAN and IEEE 802.15.4-SUN interference in urban environments. The results are based on a simple simulation that is parameterized using PHY layer measurements of controlled interference scenarios.