The integration of compressive sensing with realtime embedded systems opens new possibilities for efficient, low-power biomedical signal acquisition. This paper presents a custom hardware platform based on the RP2350 microcontroller, tailored for synchronized multi-modal biomedical monitoring. The system is capable of capturing cardiopulmonary sounds, along with biopotential signals such as phonocardiography (PCG), electrocardiography (ECG) and electromyography (EMG), photoplethysmography (PPG), and inertial measurement unit (IMU) data for posture recognition. To ensure sampleaccurate synchronization, a Sub-1GHz radio system is used across multiple nodes. Wi-Fi and Bluetooth connectivity enable centralized data aggregation. Experimental results demonstrate the achieved decrease in power consumption when using compressive sensing, efficient multi-node synchronization, and scalability for wireless biomedical monitoring applications. The compact form factor and low-cost design make it suitable for various medical applications, including remote healthcare and long-term monitoring.

In music performance research, conservatory pianists have been a subject of interest. However, previous studies have often relied on data captured from unsynchronised devices, leaving a significant portion of potential data unexplored. This paper introduces a synchronisation method for a multimodal sensor setup to address this gap. The primary focus of this paper is the implementation of a synchronization mechanism. The proposed technique is a cheap and robust way to synchronise the required multimodal setup. The insights derived from this research can improve future studies and methodologies in musical performance research and are more generically applicable towards any multimodal sensor setup.

Navigating spatially varied and dynamic environments is one of the key tasks for autonomous agents. In this paper we present a novel method of navigating a mobile platform with one or multiple 3D-sonar sensors. Moving a mobile platform and subsequently any 3D-sonar sensor on it, will create signature variations over time of the echoed reflections in the sensor readings. An approach is presented to create a predictive model of these signature variations for any motion type. Furthermore, the model is adaptive and works for any position and orientation of one or multiple sonar sensors on a mobile platform. We propose to use this adaptive model and fuse all sensory readings to create a layered control system allowing a mobile platform to perform a set of primitive motions such as collision avoidance, obstacle avoidance, wall following and corridor following behaviours to navigate an environment with dynamically moving objects within it. This paper describes the underlying theoretical base of the entire navigation model and validates it in a simulated environment with results that shows the system is stable and delivers expected behaviour for several tested spatial configurations of one or multiple sonar sensors that can complete an autonomous navigation task.

State-of-the-art autonomous vehicles mainly rely on optical sensors to perceive their environment. However, the performance of these sensors worsens dramatically in environments where airborne particles are present. Sonar sensors rely on acoustic waves which are able to pass through these distortions. The data gathered from sonar could complement the distorted data of the optical sensors in these earlier mentioned environments. In this paper we will discuss the newest 3D in-air sonar sensor developed by CoSys-Lab: the micro Real Time Imaging Sonar (μRTIS). It is the smaller version of the previously developed embedded Real Time Imaging Sonar (eRTIS) and consists of a uniform rectangular array of 5 by 6 microphones compared to the pseudo-random 32 microphone array based on Poisson Disc-Sampling used with the eRTIS. In this paper we will discuss the used hardware of the μRTIS, followed by the implemented processing algorithms to form acoustic images from reflected ultrasonic data. Furthermore, we will discuss the spatial resolution that can be obtained with the presented hardware architecture using different beamforming algorithms. Finally, we will validate the real-life performance, comparing the acoustic images of the μRTIS when using Delay-And-Sum or MUltiple SIgnal Classification to solve the different directions of arrival.

Many popular advanced sonar systems provide accurate and reliable measurements containing crucial info needed by robotic applications such as range, bearing and reflection strength of the objects in the field of view. While these sensor systems provide these crucial pieces of information accurately, they are often limited by a lack of processing power and/or size which leads to them needing an external computing device to process all the information generated by the microphone array on the sensor. In this paper we present two versions of a novel fully embedded 3D sonar sensor which have different sensing architectures which enable 3D perception for robotic application in harsh conditions using ultrasound at low cost. Experimental results taken from an office environment will show the 3D localization capabilities and performance of the sensor, showing the sensor has a large field-of-view (FoV) with accurate 3D localization combined with real-time capabilities.

In this paper we present a flexible low-cost sonar sensor platform that can be used for a wide range of biomimetic sonar experiments and autonomous sonar navigation targeted at robotics applications. The navigation abilities of bats using ultrasound (sonar) in unknown cluttered environments are very effective and can be distilled into a sensor architecture and accompanying control methodology that lends itself to be implemented on cost efficient hardware. The sensor architecture and processing methodology of this sensing platform mimics that of bats. In this paper we specifically focused on the common big-eared bat (Micronycteris microtis) although this could be transferred to other bat species or even other echolocating animals since the experimental platform was designed for flexibility. Using this platform we were able to implement a control system using a subsumption architecture that features different behavior patterns based solely on the sonar sensor as a source of exteroceptive information. In order to validate the combination of our autonomous navigation control system and our developed sonar sensor platform, the hardware was mounted on the P3DX robotics platform that was introduced in an unknown testing environment and have it drive autonomously. These experiments were used to validate our assumption of the efficacy of these relatively simple biomimetic control mechanisms and thus alleviating the need for expensive sensing platforms for certain robotics applications.

Obtaining accurate data about the environment in which a robot is located is a crucial matter when it comes to autonomous navigation and other robotic applications. A popular method of acquiring this information is to use sonar-rings, where a robot is fitted with multiple simple ultrasound transducers pointed in the directions where an object can appear. However, in a time where accurate 3D data is gaining importance, other sensing modalities are becoming more popular because of the ability to measure dense 3D point clouds. In these point clouds not only the horizontal plane is measured, but objects in the elevation planes can also be registered, which can be very interesting and makes applications such as 3D SLAM or object recognition possible. In this paper we present a way to extract complex 3D point cloud data from the entire surrounding sphere using multiple interconnected eRTIS sensors. These advanced imaging sonar sensors offer the flexibility of the popular sonar-ring in combination with the benefits of some of the competing sensing modalities. The setup presented here uses less sonar sensors (and thus less external hardware) while obtaining more information from the complete frontal hemispheres of each individual sensor. This setup is discussed, along with the issues that arise when using complex imaging sonar sensors in a network, and is tested in an indoor and outdoor environment. At the end of this paper is a discussion of the obtained results.

Pose estimation systems have seen some big developments in the last two decades due to technological advances and a greater need for these systems. The industry that thrives and simultaneously popularizes these developments is the entertainment industry. One of the latest developments in home entertainment are Virtual Reality systems that lets users have an immersive experience when playing video games. In order to fully achieve this immersive experience, objects in the ‘real world’ can be used to manipulate objects in the virtual world. Therefore the ‘real world’ objects need to be located in the user's environment to a great extent. One of these virtual reality kits utilizes an optical solution that incorporates dual-axis rotating laser sweeps on the transmitter side and photodiodes on the receiver side to achieve the positioning of these objects. Since the transmitters are stand-alone systems that can be purchased at a relatively low price, we propose to use this hardware in combination with custom low-cost receiver hardware to achieve an affordable yet precise and accurate six degrees-of-freedom human body pose estimation system.

Throughout the last decades, human motion capture systems have become an important tool for various sectors. Besides the entertainment sector, which is probably the most known sector to use these systems through popular movies and video games, the medical sector has adopted this technology as an analysis tool. These clinical analyses measure the human body posture and movement for various purposes including rehabilitation and sports, and require high accuracy and precision. Although there is a variety of products that offer these capabilities, there is not yet an affordable system that meets the requirements for medical implementations. We therefore propose using an ultrasonic six degrees-of-freedom sensor system which combines a distributed array of ultrasonic emitters and a small array of ultrasonic receivers. In order to support low-cost human pose estimation, while retaining the possibility for re-use and expandability, the need for a flexible embedded hardware platform naturally emerged. The proposed hardware design supports the requirements of a high accuracy and high precision pose estimation system while keeping its overall system cost low.

Passive acoustic localization is an important technique in a wide variety of monitoring applications, ranging from healthcare over biological survey to structural health monitoring of buildings. If the subject of interest emits a recognizable sound and is picked up by a distributed array of microphones it is possible to measure the difference in arrival times and reconstruct the source positions. This requires the microphones to be synchronized up to a fraction of the expected time differences of arrivals in order for the system to be able to produce accurate location estimates. In this paper we take a closer look at the techniques required to synchronize a network of sensors, connected through a low-power RF-communication link, in a distributed manner, ie. without the presence of a master node. The absence of a master node makes the network more robust against the failure of a single node. We took inspiration from the synchronization technique observed in some species of fireflies. A synchronized pseudo random number is embedded in the captured data as a marker to re-align the data-streams in time in a post-processing stage.