In this paper, we introduce a novel real-time and contact-free sensor system, Helena, that can be mounted on a bed frame to continuously monitor sleep activities (entry/exit of bed, movement, and posture changes), vital signs (heart rate and respiration rate), and falls from bed in a real-time and pervasive computing manner. The smart sensor senses bed vibrations generated by body movements to characterize sleep activities and vital signs based on advanced signal processing and machine learning methods. The device can provide information about sleep patterns, generate real-time results, and support continuous sleep assessment and health tracking. The novel method for detecting falls from bed has not been attempted before and represents a life-changing for high-risk communities, such as seniors. Comprehensive tests and validations were conducted to evaluate system performances using FDA approved and wearable devices. Our system has an accuracy of 99.5% detecting on-bed (entries), 99.73% detecting off-bed (exits), 97.92% detecting movements on the bed, 92.08% detecting posture changes, and 97% detecting falls from bed. The system estimation of heart rate (HR) ranged ±2.41 beats-per-minute compared to Apple Watch Series 4, while the respiration rate (RR) ranged ±0.89 respiration-per-minute compared to an FDA oximeter and a metronome.

This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in-network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices (such as Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.

Monitoring sleep quality and status is important to learn health condition for improvement and prevent sleep apnea. A bed-mounted seismometer system is proposed to monitor the heart and respiratory rates, and body movement and posture, during the sleep. To effectively monitor sleep status, an innovative local maxima statistics based approach and an instantaneous property based method are developed to estimate heart and respiratory rates, respectively. These methods are more robust and stable compared to previous works. Besides, algorithms for body movement and posture identification are also investigated based on instantaneous properties. We incorporated these algorithms to create a novel contactless sleep monitoring system that can keep track of heart rate, respiration rate, movement patterns and posture changes on events near to the bed. Our technology includes a small, powerful and low-cost smart seismometer, that can be easily installed to a bed frame under the mattress or boxes, and a user-friendly graphic interface, that can be paired with smartphone devices and display results through the APPs notifications. A prototype system is demonstrated, showing great potentials in monitoring a person's sleep status under different conditions.

This paper presents a novel in-network person identification and fall detection system that uses floor seismic data produced by footsteps and fall downs as an only source for recognition. Compared with other existing methods, our approach is done in real-time, which means the system is able to identify a person almost immediately with only one or two footsteps. An adapted in-network localization method is proposed in which sensors collaborate among them to recognize the person walking, and most importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve accuracy in person identification. Our system is innovative since it can be robust to identify fall downs from other possible events, like jumps, door close, objects fall down, etc. Such a smart system can also be connected to smart commercial devices (like Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows it is able to identify people with one or two steps in an average of 93.75% (higher accuracy than other methods that use more footsteps), and it detects fall downs with an acceptance rate of 95.14% (distinguishing from other possible events). The fall down localization error is smaller than 0.28 meters, which it is acceptable compared to the height of a person.

Monitoring and tracking the underground moving target have many important applications, such as homeland security and defense applications. A wireless seismic network is designed to sense the seismic wave generated by the moving target and locate the target based on Reverse Time Migration (RTM) method. A new Gaussian Beam Migration (GBM) algorithm is used to improve the traditional RTM algorithm. The GBM-RTM method is a time-frequency domain algorithm, where the complex time domain calculation is transformed to a simple frequency domain calculation. By using GBM-RTM method, we accelerate the computation and reduce the communication cost. Through the extensive simulations and experiments, we demonstrate the trajectory tracking of the moving underground targets. The stability and accuracy of our proposed localization algorithms are also evaluated.

Ambient Noise Seismic Imaging (ANSI) is a recently developed geophysical methodology to image the shallow subsurface structures of earth using ambient/environment noise as the source. Integrating ANSI computing within distributed sensor networks will enable real-time continuous monitoring of subsurface dynamics for sustainability studies. However, the research challenges associated with this innovative approach are significant. Traditional data collection using sensor networks imposes practical difficulty for real-time applications, because of the sheer amount of data and large-dense sensor arrays versus limited network bandwidth. This paper is the first to investigate how to utilize the computing capabilities of sensor nodes to perform the computation of ANSI under resource constraints. We explored two distributed approaches (aggregation and consensus) for computing ambient noise eikonal tomography and obtaining phase velocity maps. We performed experiments using CORE emulator to obtain phase velocity maps on real data from USArray Transportable Array. Results demonstrate that our approaches can illuminate phase velocities under network constraints. We also show that the proposed aggregation and consensus algorithms not only balance the computation load but also achieve low communication cost and high data loss tolerance.