A novel pulseoximeter for bluetooth synchronized measurements in a body sensor network

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

Siirtola

et al. 2021

Most off-the-shelf wearable devices do not provide reliable synchronization interfaces, causing multi-device sensing and machine learning approaches, e.g. for activity recognition, still to suffer from inaccurate clock sources and unmatched time. Instead of using active online synchronization techniques, such as those based on bidirectional wireless communication, we propose in this work to use the human heartbeat as a reference signal that is continuously and ubiquitously available throughout the entire body surface. We introduce PulSync, a novel approach that enables the alignment of sensor data across multiple devices utilizing the unique fingerprint-like character of the heart rate variability interval function. In an evaluation on a dataset from 25 subjects, we demonstrate the reliable alignment of independent ECG recordings with a mean accuracy of −0.71 ± 3.44 samples, respectively −2.86 ± 11.43 ms at 250 Hz sampling rate.

Section: A Present Synchronization Methodsmentioning

confidence: 99%

“…To enable this search by means of an approved tool, namely the cross-correlation, the sequences are first transformed into HRV interval functions by applying equation (4).…”

Section: Synchronization Methodsmentioning

confidence: 99%

PulSync: The Heart Rate Variability as a Unique Fingerprint for the Alignment of Sensor Data Across Multiple Wearable Devices

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

Siirtola

et al. 2021

“…Not only the methods applied in wireless sensor networks (WSN), as surveyed in Sundararaman et al (2005) and Lasassmeh and Conrad (2010), but in particular those running on wearable devices, as discussed in Zheng et al (2014), primarily focus on energy efficiency. As discussed in Barth et al (2008), Mare andKotz (2010), andNaganawa et al (2015), radio transmission, applying e.g., the popular Bluetooth in Roberto Casas (2005), Ringwald and Romer (2007), and Pflugradt et al (2014), suffers largely from a variable path loss, shadowing effects due to the devices' direct attachment to the water-rich human body. Offline synchronization methods, in contrast, use external events to align recorded signals in a post-processing step.…”

Section: Synchronizationmentioning

confidence: 99%

IBSync: Intra-body synchronization and implicit contextualization of wearable devices using artificial ECG landmarks

2022

Front. Comput. Sci.

With a smaller form factor and a larger set of applications, body-worn devices have evolved into a collection of simultaneously deployed hardware units, rather than into a single all-round wearable. The sensor data, logged by such devices across the user's body, contains a wealth of information but is often difficult to synchronize. Especially the application of machine learning techniques, e.g., for activity recognition, suffers from the inaccuracy of the devices' internal clocks. In recent years, intra-body communication emerged as a promising alternative to the traditional wired and wireless communication techniques. Distributed wearable systems will notably benefit from its advantages, such as a superior energy efficiency. However, due to the absence of commercially available platforms, applications using this innovative technique remain rare and underinvestigated. With IBSync, we present a novel concept in which artificial landmark signals are received by body-worn devices on touching, approaching, or passing certain areas, surfaces, or objects with embedded transmitter beacons. The landmark signals enable both the wearables' intentional or incidental synchronization as well as the implicit contextualization using supplementary information about the beacons' situational context. For the detection of the landmarks, we propose to repurpose the on-board ECG sensor front-end available in recent commercial wearable devices. Evaluated on a total of 215 min of recordings from two devices, we demonstrate the concept's feasibility and a promising synchronization error of 0.80±1.79 samples or 6.25±14.00 ms at a device's sampling rate of 128 Hz.

“…Because of the limited resources on board, the methods for wireless sensor networks (WSN) [27,47] primarily focus on energy efficiency [55]. Nevertheless, also the mostly applied Bluetooth [41,43,44] suffers largely from shadowing effects due to the devices' direct attachment to the water-rich human body [6,31,37]. Offline synchronization methods, in contrast, use external incidents to align the signal channels afterwards, in a post-processing step.…”

Section: Synchronization Techniquesmentioning

confidence: 99%

IBSync: Intra-body Synchronization of Wearable Devices Using Artificial ECG Landmarks

Huynh

2021 International Symposium on Wearable Computers

2021

The synchronization of wearable devices in distributed, multi-device systems is a persistent challenge. Particularly machine learning approaches suffer from the devices' inaccurate clock sources and unmatched time. While the online synchronization based on radio transmission is energy-intensive, offline approaches originated in activity recognition suffer from inaccurate motion patterns. In recent years, intra-body communication emerged as a promising technique that uses the human body as a limited and hence more efficient medium. Due to the absence of commercial platforms, applications are rare and underinvestigated. To boost their development and to enable the precise synchronization, we introduce IBSync and propose to repurpose the ECG sensor in commercial wearable devices to detect artificial signals induced into the skin. The shorttime Fourier transform and Pearson's normalized cross-correlation are used to detect, precisely locate, and assign synchronization landmarks within the measurements. Based on a total of 105 min of recordings, we evaluated the concept and demonstrate its general feasibility with a promising accuracy of 0.203 ± 1.633 samples (1.587 ± 12.755 ms) in typical proximity to the transmitter. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile devices; • Hardware → Digital signal processing.