Northeastern University to Transfer Two Patents for Wearable Health Monitoring Devices at RMB 300,000
Recently, Northeastern University released a public notice on the transfer of its scientific and technological achievements, proposing to commercialize two self-developed invention patents through transaction, with the proposed transaction price being300,000 yuan. The two patents transferred in this transaction both focus on the fields of human motion monitoring and health surveillance, namely“A Single-Arm ECG-Assisted Fall Detector” “A Wearable Human Motion State Data Monitoring System and Method”, the core R&D team includesXu Lisheng et al.Multiple researchers.
Image from the official website of Northeastern University
From the perspective of technological innovation,Single-Arm ECG-Assisted Fall DetectorIt breaks through the traditional single-mode fall detection that relies solely on acceleration sensing, innovatively integrating 3D acceleration sensing with single-arm ECG signal acquisition technology to achieve dual monitoring of postural changes and physiological indicators. Furthermore, the single-arm wearable design significantly enhances portability.Wearable System and Method for Monitoring Human Motion State DataThis addresses the technical challenge of sensor coordinate system deviation in motion monitoring by establishing a triaxial acceleration calibration model through voltage compensation and tilt angle calculation, converting data from the device’s own coordinate system to the physical coordinate system for more accurate acquisition of human motion status data. Both technologies employ the ZigBee wireless communication protocol for data transmission, inMedical Monitoring and Motion Status MonitoringAchieved dual innovation in technology and practicality.
Against the backdrop of population aging, falls among the elderly have become a prevalent health issue. Falls not only cause physical injuries such as fractures and soft tissue damage but can also lead to severe complications, including intracranial hemorrhage and organ damage, potentially resulting in disability or death. Timely response during the golden hour for treatment after a fall can significantly reduce the severity of injuries and mortality rates. Meanwhile, precise monitoring of human motion has become a core requirement in fields such as elderly care, sports rehabilitation, and clinical monitoring. The practical implementation and optimization of technologies in these two areas have become urgent issues to be addressed in clinical practice.
In existing clinical and application solutions, the mainstream approaches for fall detection in the elderly are divided into two categories,One category is a single-posture monitoring solution based on 3D accelerometers and gyroscopes., detects postural changes by sensing variations in acceleration along the X, Y, and Z axes of the human body, thereby identifying fall events;Another category comprises monitoring solutions that integrate ECG signals in a minority of cases., assisted by physiological indicators such as heart rate and ECG waveforms for judgment, but ECG signal acquisition still adheres to the traditional 3-lead, 5-lead, or even 12-lead methods, requiring electrodes to be placed on multiple body sites and connected to the acquisition device via lead wires.
and inHuman Motion State MonitoringIn this regard, most existing solutions employ triaxial accelerometers to collect motion data. Some approaches integrate sensors into carriers such as smartphones and wristbands, while others utilize standalone wearable sensor designs. Certain technologies optimize data through ripple reduction and noise-filtering circuits, or employ interpolation methods to enhance data accuracy. A few solutions have also attempted to combine acceleration and electrocardiogram (ECG) signals for comprehensive monitoring. However, all current approaches exhibit significant clinical and application-related limitations, failing to meet practical needs.
In the field of fall detection,Single-accelerometer-based fall detection schemes rely solely on changes in body posture to identify falls, lacking integration with physiological indicators for comprehensive analysis. This approach is prone to misclassifying normal activities such as squatting and walking as falls, while also risking missed detections when no significant postural change occurs after a fall, resulting in extremely low monitoring accuracy. In contrast, ECG-integrated detection solutions, which adhere to traditional multi-lead ECG acquisition methods, involve dispersed electrode placement and cumbersome lead wires. These limitations severely restrict daily physical activities, preventing true wearable and portable monitoring, and making them ill-suited for everyday scenarios faced by the elderly, whether at home or outdoors.
In the field of human motion state monitoring,Existing solutions generally suffer from a lack of data calibration. The local coordinate system of wearable sensors tends to deviate from the physical coordinate system due to human movement and variations in wearing position. Furthermore, most solutions overlook the impact of battery voltage fluctuations on data acquisition accuracy, resulting in significant errors in acceleration data. Some approaches that integrate sensors into non-fixed carriers, such as smartphones, further exacerbate measurement deviations due to the loose coupling and variable positioning relative to the human body. Even though certain solutions implement signal optimization at the circuit level, they fail to achieve effective data calibration at the system level, thereby unable to accurately reflect the actual changes in human motion data.
Consequently, there is a clear and urgent clinical and market demand for such technologies. Specifically, for elderly fall detection, monitoring solutions must combine high precision with portability. These systems should integrate postural and physiological metrics to achieve accurate fall identification, while simplifying ECG acquisition to enable miniaturized, wearable designs suitable for unconstrained daily use by the elderly. Furthermore, they must feature automatic alerting capabilities to promptly send warning notifications to caregivers in the event of a fall.
On the other hand, for human motion state monitoring, wearable monitoring solutions capable of precise data calibration are required to address data errors such as coordinate system offset and voltage interference, thereby outputting authentic and reliable human motion data that meet the stringent accuracy requirements for motion data in scenarios including elderly care, sports rehabilitation, and clinical condition monitoring.
Therefore, we now needAn Integrated Wearable Device for Fall Detection and Human Motion Monitoring in the Elderly, Featuring Multi-Dimensional Sensing and Precise Calibration Algorithms, to address the core pain points of existing fall detection solutions, including high rates of misjudgment and missed detections, cumbersome and non-portable ECG acquisition, as well as coordinate system shifts, voltage interference, insufficient accuracy, and the inability to truly reflect human motion data in human movement status monitoring.
The two invention patents transferred in this transaction are centered on human motion monitoring and health surveillance, forming a complementary technological synergy. They have achieved multiple innovative breakthroughs in technical design, detection accuracy, and practical adaptability, demonstrating significant technical advantages and application value compared to traditional monitoring technologies and devices. The core innovations and technical strengths are reflected in multi-dimensional technological advancements and practical design implementations.
Regarding single-arm ECG-assisted fall detectors:
First, it innovatively achieves dual-dimensional integrated detection of body position and physiological indicators,Breaking through the traditional fall detection model that relies solely on 3D accelerometers to sense postural changes, this approach integrates the patterns of heart rate variability during falls by combining 3D accelerometer-based posture monitoring with single-arm ECG signal acquisition. By comprehensively analyzing acceleration data and ECG characteristic parameters such as P waves and S waves, fall events are accurately identified. This significantly improves the accuracy of fall detection, effectively avoiding false positives from normal activities like squatting and walking, as well as missed detections after actual falls.
Second, it revolutionized the method of ECG signal acquisition,Abandoning the traditional multi-site acquisition modes of 12-lead, 5-lead, and 3-lead ECGs, this system achieves single-arm, two-lead ECG acquisition using only two electrode patches on the left upper arm. This design eliminates the constraints of lead wires and, combined with a cuff-style wearable configuration, enables true wearability and portability without interfering with the daily activities of the monitored individual.
Third, it has developed a highly integrated signal processing system.Through a multi-stage signal conditioning circuit comprising pre-amplification, band-pass filtering, main amplification and filtering, notch filtering, and level shifting, the weak single-lead ECG signal is amplified with a high gain of 82.9 dB. This process effectively eliminates electromyographic (EMG) noise, 50 Hz power-line interference, and low-frequency respiratory noise, thereby ensuring the validity of the ECG signal. Furthermore, the entire device is powered by only four AA batteries, with its low-power design enhancing practicality for portable use.
Fourth, an intelligent detection and alarm system was established.Real-time transmission of monitoring data is achieved via the ZigBee wireless communication protocol. The system control center initially determines the type of fall based on acceleration data, and then makes a final confirmation by combining electrocardiogram (ECG) signal parameters. Upon confirming a fall, the system automatically sends an alert SMS to caregivers through a GSM modem, enabling rapid response and gaining valuable time for medical treatment.
In terms of wearable systems and methods for monitoring human motion state data:
First, it overcame the technical challenges of calibrating motion monitoring data by pioneering a triaxial accelerometer calibration scheme that combines battery voltage compensation with tilt angle calculation.First, voltage compensation is performed based on the difference between the battery voltage in the horizontal state and that in the operating state of the device, thereby eliminating the impact of battery voltage fluctuations on data acquisition accuracy. Subsequently, by calculating the tilt angles of the X and Y axes between the device’s intrinsic coordinate system and the physical coordinate system, a three-axis acceleration calibration model is established to transform acceleration data from the device’s intrinsic coordinate system to the physical coordinate system. This approach systematically addresses the lack of data calibration inherent in traditional monitoring technologies.
Second, it enables precise collection of movement data., through coordinate system transformation and data calibration, effectively eliminated the offset errors of the device coordinate system caused by wearing position and human movements, enabling the collected X-, Y-, and Z-axis acceleration data to accurately reflect human motion changes in the anterior-posterior, medial-lateral, and vertical directions. This significantly improved the accuracy of motion state monitoring and provided reliable data support for human motion posture recognition and behavior analysis.
Thirdly, it adopts an independent monitoring design worn at the waist.Securing the monitoring device firmly to the human waist significantly reduces measurement deviations caused by positional shifts, compared to solutions that integrate sensors into non-fixed carriers such as mobile phones. Coupled with the ZigBee wireless communication protocol, the system supports simultaneous multi-device monitoring and real-time data transmission to a PC, thereby fulfilling the dual requirements of precise single-subject monitoring and synchronized multi-subject monitoring.
Fourth, it designs standardized monitoring procedures and fault-tolerance mechanisms.Standardization of device wear is achieved through tilt calibration prior to monitoring; if the calibrated tilt angle exceeds the alarm threshold, an automatic alert prompts adjustment of the wearing position. During monitoring, battery voltage data is updated in real time according to the acquisition cycle, and compensation calibration is performed accordingly, ensuring the continuity and accuracy of monitoring data and making the technical solution better suited for practical application scenarios.
Furthermore, both patented technologiesData transmission is implemented using the ZigBee wireless communication protocol., combining the transmission advantages of low power consumption and high stability, with all core hardware selected fromMature Industrial-Grade Chips and Sensors, while ensuring technical performance, it possesses a solid foundation for industrialization. Furthermore, the two technologies create synergistic effects in fall detection and motion monitoring. They can be applied independently in various scenarios such as elderly care, sports rehabilitation, and clinical monitoring, or used in combination to achieve comprehensive monitoring of human movement status and precise early warning of fall risks, demonstrating strong technical adaptability and scalability.
The two patents have been approved through“Multi-Dimensional Sensor Fusion” “Systematic Data Calibration” “Lightweight Wearable Design”Three Core Innovations: Bridging the Gap Between Consumer-Grade Accuracy and Professional Usability for Enhanced Competitiveness in Elderly Care, Sports Rehabilitation, and Clinical Monitoring
Philips Sports Rehabilitation Monitoring System(Professional Medical-Grade Sports Rehabilitation Monitoring Solution) adopts a combination of a waist-worn independent sensor and supporting PC-based analysis software. The sensor unit integrates only a triaxial accelerometer, with no other physiological monitoring modules. Collected data is synchronized to the PC via wireless transmission, relying on the software to achieve visual presentation of movement data and assessment of movement standardization.
withThree-Axis Accelerometer Worn at the WaistCentered on this core, the system collects raw triaxial (X, Y, Z) acceleration data during human movement. By leveraging preset motion thresholds and trajectory models within PC-based analysis software, it evaluates the range, frequency, and standardization of limb movements during rehabilitation training, thereby providing data-driven references for therapists to formulate training plans. Primarily targeted at hospital rehabilitation departments and specialized rehabilitation institutions, the system is suitable for monitoring motor rehabilitation in patients with stroke, musculoskeletal injuries, and postoperative limb functional impairment, with a focus on assessing the correctness of basic movements in clinical rehabilitation settings.
The Freescale MMA7260 sensor kit is an open electronic component kit,At its core, the product consists of a bare-die triaxial accelerometer paired with basic circuit modules. It lacks an integrated wearable enclosure or finished-product design, requiring users to independently provide power supply, data transmission, and packaging components. As a foundational product extending industrial-grade components toward the consumer segment, it has no fixed wearable form factor. Primarily targeted at electronics enthusiasts, university experimental teaching, and small-scale device R&D teams, it is intended for basic prototype development and experimental testing related to motion monitoring. It cannot be directly deployed in real-world scenarios such as daily consumer motion tracking or clinical monitoring for medical rehabilitation, but serves as a underlying component providing a technical foundation.
In the current market, the two patent technologies transferred by Northeastern University precisely address key market pain points and demonstrate strong adaptability for practical implementation. Specifically, the single-arm ECG-assisted fall detector targets the critical need for elderly fall prevention, overcoming the limitations of consumer-grade smartbands (which are prone to false alarms) and traditional medical-grade devices (which are inconvenient to wear). Its design, featuring single-arm signal acquisition and dual-dimensional verification, offers significant differentiated advantages in home-based and community elderly care settings. Driven by population aging, supportive policies for smart elderly care, and growing demand for sports health and rehabilitation medicine, the market for wearable human monitoring products is experiencing explosive growth. Application scenarios for high-precision monitoring technologies continue to expand. Furthermore, the technological trends toward miniaturization, low power consumption, and wireless transmission in wearable devices align closely with the concepts embodied in these two patents. Their wireless communication and modular hardware designs hold substantial potential for industrialization, enabling iterative upgrades and expansion into diverse application scenarios through integration with multiple technologies.