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Bodyseismography (BSG) is a term used in recent physiological monitoring research for the measurement and analysis of mechanical vibrations generated by the human body and transmitted through supporting structures, particularly beds.[1] Such vibrations can include components associated with cardiac activity, respiration, and body movement. In bed-based systems, sensitive seismic or vibration sensors can be mounted on or beneath a bed rather than attached directly to the subject.[2]
Ballistocardiography (BCG) and seismocardiography (SCG) are established cardiomechanical sensing methods used to measure mechanical activity associated with the cardiovascular system.[3] Modern BCG measurements have been obtained using beds, mattresses, chairs, pillows, weighing scales, and other supporting structures for unobtrusive physiological monitoring.[4] Bodyseismography(BSG) contains the information of both BCG and SCG due to the high-sensitivity of geophone and tri-axis geophone array designs.[5]
Background
[edit]Mechanical signals generated by physiological processes have long been studied using cardiomechanical sensing methods. Ballistocardiography measures whole-body motion associated with cardiovascular forces, whereas seismocardiography generally measures local chest-wall vibrations caused by cardiac mechanical activity.[3]
Advances in sensing hardware and signal processing have enabled BCG and SCG measurements outside conventional clinical environments. Reviews of BCG and bed-based physiological sensing describe sensors integrated into beds, mattresses, chairs, pillows, and weighing scales, together with methods for extracting cardiac and respiratory information from mechanically measured signals.[4][6]
Earlier studies demonstrated that geophones mounted on beds could be used to acquire physiological information from bed vibrations. In 2016, the HB-Phone system used off-the-shelf analog geophones installed under a bed to detect heartbeats during sleep.[7] The later VitalMon system used geophones to estimate both heart rate and respiratory rate, including when two people shared the same bed.[8]
Bed-mounted seismic sensing is one approach to acquiring mechanical physiological signals without placing electrodes or wearable sensors on the body. A system reported in the IEEE Internet of Things Journal used a bed-mounted seismic sensor to estimate bed occupancy, heart rate, respiratory rate, and inter-beat interval.[2]
The term Bodyseismography subsequently appeared in published work describing under-bed seismic sensing of body-generated vibrations. A 2026 study in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies used an under-bed horizontal seismic sensor to capture micro-vibrations associated with respiration, heartbeat, and movement for sleep-apnea-related classification.[1]
Measurement principle
[edit]In bed-based seismic sensing, physiological processes such as cardiac activity, respiration, and body movement generate mechanical vibrations that can be transmitted through the body into a supporting structure and detected by sensitive sensors.[9][1]
A seismic sensor or other vibration sensor may be attached to the bed frame rather than directly to the subject.[2] The resulting signal can contain overlapping contributions from cardiac activity, respiration, body movement, and environmental or structural vibration, so signal processing is used to identify usable segments and extract physiological information.[4][9]
Unlike electrocardiography (ECG), which records the electrical activity of the heart, BCG, SCG, and related mechanical sensing methods measure mechanical manifestations of physiological activity. Bed-based mechanical signals can vary with factors including body movement, mattress characteristics, sensing configuration, and sensor placement.[3][4]
Relationship to ballistocardiography and seismocardiography
[edit]Ballistocardiography traditionally refers to measurement of body motion resulting from cardiovascular forces, particularly motion associated with the movement of blood through the cardiovascular system.[3] Modern BCG systems may also contain respiratory and movement information, particularly when sensing is performed through beds or mattresses.[4]
Seismocardiography generally refers to local chest-wall vibrations associated with the mechanical activity of the heart.[3] Bed-based seismic sensing instead measures vibrations after transmission into the bed or another supporting structure.[7][9]
A 2026 preprint explicitly described triaxial Bodyseismography as an extension of BCG. The proposed system measured mechanical signals along three axes and modeled three-dimensional wave propagation through the body–bed system for blood-pressure estimation.[5]
Applications
[edit]Vital-sign monitoring
[edit]Bed-mounted mechanical sensing has been studied for unobtrusive monitoring of cardiac and respiratory activity.[7][8] A system reported by Song et al. used a bed-mounted seismic sensor together with signal-quality assessment and physiological signal-processing methods to estimate heart rate, respiratory rate, and inter-beat interval while also detecting bed occupancy.[2]
Because the sensor is attached to the supporting structure rather than to the body, such systems can acquire signals without electrodes or a body-worn sensing device. Body movement, mattress properties, environmental vibration, and sensing configuration can affect the acquired signal and its quality.[4][2]
Blood-pressure monitoring
[edit]Mechanical signals acquired from beds have also been investigated for continuous blood pressure estimation. In 2024, a study using a bed-mounted seismic sensor extracted time-series features from bedseismogram signals and applied machine-learning models to estimate systolic and diastolic blood pressure.[9]
A 2026 preprint proposed a physics-constrained deep learning framework based on triaxial Bodyseismography. The method incorporated a model of three-dimensional wave propagation through the body–bed system and used relationships among sensing axes as constraints during model training.[5]
Sleep and respiratory monitoring
[edit]Bodyseismography has been investigated for sleep-related respiratory monitoring. A 2026 feasibility study used an under-bed horizontal seismic sensor to acquire mechanical signals associated with respiration, heartbeat, and movement from 116 subjects.[1] The study extracted respiratory, heartbeat, and movement features and formulated a three-class classification task distinguishing normal breathing, obstructive sleep apnea or hypopnea, and central sleep apnea.[1]
The use of bed-based mechanical sensing for respiratory and sleep monitoring predates the use of the term Bodyseismography. Reviews of BCG systems have documented mattress- and bed-integrated sensors used for respiratory-rate estimation and sleep-related physiological monitoring.[4][6]
Signal processing
[edit]Signals acquired through bed-mounted mechanical sensors may contain overlapping components associated with cardiac activity, respiration, body movement, and environmental vibration. Methods used in BCG and related bed-based sensing research include filtering, peak detection, time-domain and frequency-domain analysis, signal decomposition, feature extraction, signal-quality assessment, and machine-learning methods.[4][6]
In a 2024 bedseismogram blood-pressure study, preprocessing and machine-learning methods were used to derive cardiovascular information from seismic measurements of the bed frame.[9] The 2026 Bodyseismography sleep-apnea study extracted respiration-, heartbeat-, and movement-related features for apnea-related state classification.[1]
A 2026 physiological peak-detection study included Bodyseismography alongside electrocardiography, photoplethysmography, and ballistocardiography in a cross-modal framework based on instruction-tuned large language models.[10]
Terminology
[edit]The abbreviation BSG has been used for more than one related term in the literature. A 2024 study used BSG as an abbreviation for bedseismogram, referring to micro-vibrations measured from a bed frame using a seismic sensor.[9]
More recent publications have used BSG as an abbreviation for Bodyseismography.[1][10] A 2026 preprint likewise used BSG for triaxial Bodyseismography and described it as an extension of BCG.[5]
See also
[edit]References
[edit]- 1 2 3 4 5 6 7 Song, Yingjian; Chen, Jiayu; Zeng, Zixuan; Zhang, Yida; Pitafi, Zaid; Das, Deepak Kumar; Phillips, Bradley G.; Kwon, Younghoon; Healy, William J.; Zhang, Xiang; Dou, Fei; Song, WenZhan (2026). "Contactless Sleep Apnea Detection with Bodyseismography". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 10 (2): 59:1–59:31. doi:10.1145/3810211.
- 1 2 3 4 5 Song, Yingjian; Li, Bingnan; Luo, Dan; Xie, Zaipeng; Phillips, Bradley G.; Ke, Yuan; Song, Wenzhan (2024). "Engagement-Free and Contactless Bed Occupancy and Vital Signs Monitoring". IEEE Internet of Things Journal. 11 (5): 7935–7947. doi:10.1109/JIOT.2023.3316674. PMC 11162756. PMID 38859814.
- 1 2 3 4 5 Inan, Omer T.; Migeotte, Pierre-François; Park, Kwang-Suk; Etemadi, Mozziyar; Tavakolian, Kouhyar; Casanella, Ramon; Zanetti, John; Tank, Jens; Funtova, Irina; Prisk, G. Kim; Di Rienzo, Marco (2015). "Ballistocardiography and Seismocardiography: A Review of Recent Advances". IEEE Journal of Biomedical and Health Informatics. 19 (4): 1414–1427. doi:10.1109/JBHI.2014.2361732. PMID 25312966.
- 1 2 3 4 5 6 7 8 Sadek, Ibrahim; Biswas, Jit; Abdulrazak, Bessam (2019). "Ballistocardiogram signal processing: a review". Health Information Science and Systems. 7 (1): 10. doi:10.1007/s13755-019-0071-7. PMC 6522616. PMID 31114676.
- 1 2 3 4 Zhang, Yuanyuan; Zhang, Yida; Li, Jiahui; Wu, Yuyan; Dou, Fei; Xiao, Yin; An, Zhenlin; Noh, Hae Young; Song, Wenzhan (2026). "Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography". arXiv:2608.23562 [eess.SP].
- 1 2 3 Recmanik, Michaela; Martinek, Radek; Nedoma, Jan; Jaros, Rene; Pelc, Mariusz; Hajovsky, Radovan; Velicka, Jan; Pies, Martin; Sevcakova, Marta; Kawala-Sterniuk, Aleksandra (2024). "A Review of Patient Bed Sensors for Monitoring of Vital Signs". Sensors. 24 (15): 4767. doi:10.3390/s24154767. PMC 11314724. PMID 39123813.
- 1 2 3 Jia, Zhenhua; Alaziz, Musaab; Chi, Xiang; Howard, Richard E.; Zhang, Yanyong; Zhang, Pei; Trappe, Wade; Sivasubramaniam, Anand; An, Ning (2016). "HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System". 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks. IEEE. doi:10.1109/IPSN.2016.7460676.
- 1 2 Jia, Zhenhua; Xu, Chenren; Bonde, Amelie; Wang, Jingxian; Li, Sugang; Zhang, Yanyong; Howard, Richard E.; Zhang, Pei (2017). "Monitoring a Person's Heart Rate and Respiratory Rate on a Shared Bed Using Geophones". Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery. pp. 6:1–6:14. doi:10.1145/3131672.3131679.
- 1 2 3 4 5 6 Song, Yingjian; Li, Bingnan; Luo, Dan; Brewster Glasgow, Glenna S.; Phillips, Bradley G.; Ke, Yuan; Song, Wenzhan (2024). "Real-Time Continuous Blood Pressure Estimation with Contact-Free Bedseismogram". ICC 2024 - IEEE International Conference on Communications. IEEE. pp. 214–219. doi:10.1109/ICC51166.2024.10622995. PMC 11583795. PMID 39583890.
- 1 2 Li, Jiahui; Zhang, Yida; Zeng, Zixuan; Chen, Jiayu; Song, Yingjian; Xiao, Yin; Dong, Nishan; Lu, Junjie; Kwon, Younghoon; Zhang, Xiang; Lu, Jin; Song, WenZhan; Dou, Fei (2026). "Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Signal". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 10 (2): 1–44. doi:10.1145/3810224.