A data-driven synchronization technique for cyber-physical systems

Bennett, Terrell R.; Gans, Nicholas; Jafari, Roozbeh

doi:10.1145/2756755.2756763

Cited by 9 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It already achieved a synchronization error of about 0.3 s for more than 80 % of the data. Based on this fundamental concept, more sophisticated approaches use specific motion patterns, e.g., in Bennett et al (2015a), Bennett et al (2015b), Wang et al (2019), andLu et al (2020), or even cough events as in Ahmed et al (2020). These achieved typical synchronization errors ranging from 250 ms down to 46 ms.…”

Section: Synchronizationmentioning

confidence: 99%

“…As detailed by Barth et al (2008), Mare and Kotz (2010), and Naganawa et al (2015), the available methods, based on Bluetooth and other popular wireless protocols, suffer from the general inefficiency of radio transmission due to a lossy air channel and, particularly in wireless body area networks (WBAN), a vicinity to the waterrich tissue. Originated in research on human activity recognition by Bannach et al (2009), the existing methods of Bennett et al (2015a), Bennett et al (2015b), Wang et al (2019), and Ahmed et al (2020) allow for the alignment of measurements offline, after the recording. However, the performed synchronization actions, i.e., gestures and motion patterns, are not incidental but rather tend to be cumbersome and obtrusive.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Wolling

Laerhoven

2022

Front. Comput. Sci.

View full text Add to dashboard Cite

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.

show abstract

Section: Synchronizationmentioning

confidence: 99%

mentioning

confidence: 99%

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

Wolling

Laerhoven

2022

Front. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…Originated in activity recognition, Bannach et al [5] established the concept of aligning time series through the correlation of specific motion patterns such as clapping, shaking, or jumping. In recent years, more advanced methods using motion patterns [8,9,30,50] or cough events [1] have been proposed. Even the white noise inherently present in physiological signals was proposed for the correlation and alignment of signal channels [49].…”

Section: Synchronization Techniquesmentioning

confidence: 99%

“…Moreover, available methods based on wireless communication, of which Bluetooth is the most popular one, suffer from the general inefficiency of radio transmission due to the lossy air channel and a vicinity to the waterrich tissue, particularly in wireless body area networks (WBAN) [6,31,37]. Originated in activity recognition, there exist methods to align measurements offline, after the recording [1,5,8,9,50]. The used gestures and motion patterns are, however, not incidental but rather tend to be cumbersome, obtrusive, and suffer from inaccuracies due to soft tissue deformation and delays due to motion sequences and inertia of the body parts [30,53].…”

Section: Introductionmentioning

confidence: 99%

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

Wolling

Huynh

Laerhoven

2021

2021 International Symposium on Wearable Computers

View full text Add to dashboard Cite

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.

show abstract

“…Such a redundancy also provides a basis to observe the same events over different data streams. This option is investigated by Bennett et al [ 147 ], and inertial data streams of different sensors affected by the same human actions are used for identifying the instance a specific event has happened. The authors show that these correlated time series can be used for identifying alignment points of time, and based on these time instances time offset and clock skew can be compensated.…”

Section: Time Synchronization Messagingmentioning

confidence: 99%

Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Yi̇ği̇tler

Badihi

Jäntti

2020

Sensors

View full text Add to dashboard Cite

Internet of Things (IoT) is expected to change the everyday life of its users by enabling data exchanges among pervasive things through the Internet. Such a broad aim, however, puts prohibitive constraints on applications demanding time-synchronized operation for the chronological ordering of information or synchronous execution of some tasks, since in general the networks are formed by entities of widely varying resources. On one hand, the existing contemporary solutions for time synchronization, such as Network Time Protocol, do not easily tailor to resource-constrained devices, and on the other, the available solutions for constrained systems do not extend well to heterogeneous deployments. In this article, the time synchronization problems for IoT deployments for applications requiring a coherent notion of time are studied. Detailed derivations of the clock model and various clock relation models are provided. The clock synchronization methods are also presented for different models, and their expected performance are derived and illustrated. A survey of time synchronization protocols is provided to aid the IoT practitioners to select appropriate components for a deployment. The clock discipline algorithms are presented in a tutorial format, while the time synchronization methods are summarized as a survey. Therefore, this paper is a holistic overview of the available time synchronization methods for IoT deployments.

show abstract

A data-driven synchronization technique for cyber-physical systems

Cited by 9 publications

References 7 publications

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

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

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

Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Contact Info

Product

Resources

About