719 results about "Fall detection" patented technology

A home-based remote care solution provides sensors including a basic health monitor (BHM) that is a measurement and feedback system. The BHM operates with low power integrated communications combined with an in-home, low power mesh network or programmable digital assistant (PDA) with cell phone technology. A cognitive system allows remote monitoring of the location and the basic health of an individual. The BHM measures oxygen saturation (SaO2), temperature of the ear canal, and motion, including detection of a fall and location within a facility. Optionally, the BHM measures CO2, respiration, EKG, EEG, and blood glucose. No intervention is required to determine the status of the individual and to convey this information to care providers. The cognitive system provides feedback and assistance to the individual while learning standard behavior patterns. An integrated audio speaker and microphone enable the BHM to deliver audio alerts, current measurements, and voice prompts. A remote care provider can deliver reminders via the BHM. The device may be worn overnight to allow monitoring and intervention. Through the ability to inquire, the cognitive system is able to qualify events such as loss of unconsciousness or falls. Simple voice commands activate the device to report its measurements and to give alerts to care providers. Alerts from care providers can be in a familiar voice to assist with compliance to medication regimens and disease management instructions. Simple switches allow volume control and manual activation. The device communicates with a series of low-power gateways to an in-home cognitive server and point-of-care (POC) appliance (computer). Alone the BHM provides basic feedback and monitoring with limited cognitive capabilities such as low oxygen or fall detection. While connected to the cognitive server, full cognitive capabilities are attained. Full alerting capability requires the cognitive server to be connected through an Internet gateway to the remote care provider.

A method and system for monitoring. The monitoring system may include at least one wearable monitor, which may include an accelerometer, coupled to a user; at least one server communicatively coupled using a network to the at least one wearable monitor, the accelerometer facilitating detection of a fall by the user and transmitting notification of said fall to said at least one server, and the server allowing verification of the fall and adjustment of fall detection parameters either automatically or through a user interface.

A user wearable portable communication device and a method for collecting and transmitting a plurality of physiological parameters to a remote monitoring center are disclosed. The portable communication device includes a provision for enabling the user to establish a video conference, a fall detection module in conjunction with an accelerometer recognizes a placement of the portable communication device, a home automation module for enabling the user to remotely operate multiple electronic devices, an acknowledgment acceptance module for receiving the acknowledgment, a physiological data collecting module communicatively coupled to a fetal doppler module for monitoring and transmitting the physiological parameters associated with a child, multiple preprogrammed contact numbers for enabling the user to establish a communication, a subscriber identity module for enabling the user establish a communication and data transmission between the portable communication device and the remote monitoring center.

The invention discloses an intelligent home based old person caring device and belongs to the technical field of Internet of things. The device comprises a sign detection terminal, a fall detection terminal, a remote server and a personal mobile terminal, wherein the sign detection terminal is used for monitoring pulse, body temperature and heart rate data of a human body in real time and sending the data to the remote server; the fall detection terminal is used for monitoring acceleration, posture change and geographical position of the human body in real time, and sending alarm information and position information to the remote server and the personal mobile terminal when detecting the occurrence of fall, and a user receives falling condition information of old persons in real time through the personal mobile terminal and logging in of the remote server. The device can realize remote access, monitoring and positioning to the device user by medical staff and family members via dependence on the Internet of things about intelligent home while monitoring physical conditions and falling conditions of the old persons, and performs real-time attention on the health status of the old persons, so as to provide rescue and help in the shortest time conveniently.

The invention discloses a wearing type dynamic real-time fall detection method and device. The wearing type detection device comprises a specific hardware structure consisting of a foot part, a waist part, a wrist part and a back part. According to the dynamic real-time fall detection method, real-time judgment of the fall of human body in any state can be finished. The core algorithm of the detection device comprises a threshold opening logic structure caused by sudden change of gait acceleration, a human body posture dynamic stability judgment algorithm based on zero moment point (ZMP), and a fall mode identification algorithm based on a human body motion database. A complete dynamic real-time detection system is constructed, and the aims of wide application field, low dependence on the application environment and real-time detection are fulfilled. Moreover, the detection method and the detection device are high in accuracy, and have the advantages of low system cost, small system size and good convenience in wearing.

The invention discloses an intelligent health service system and an alarming reliable detection method. The method comprises a falling detection method and a platform alarming method. The falling detection method comprises the following steps: collecting an accelerated speed and angular speed data of a wearer at fixed time, monitoring a parameter and recording a changing data sequence; judging if a falling process exceeds a falling threshold value thereof; if the falling process exceeds the falling threshold value, judging if heart rate is abnormal, and if the heart rate belongs to an abnormal scope, setting a high-risk-level falling alarm; otherwise, setting a medium-risk-level falling alarm; if the falling process does not exceed the falling threshold value, setting a low-risk-level falling alarm; sending a falling alarm event and a physiological sign data of the wearer to a cloud health service platform. An intelligent wearing device provided by the invention can accurately confirm if a serious falling event occurs, a mistakenly reported and low-influence falling event is effectively filtered, and the alarm answering and processing pressures of the cloud health service platform and seat mould are reduced.

A method and system for monitoring. The monitoring system may include at least one wearable monitor, which may include an accelerometer, coupled to a user; at least one server communicatively coupled using a network to the at least one wearable monitor, the accelerometer facilitating detection of a fall by the user and transmitting notification of said fall to said at least one server, and the server allowing verification of the fall and adjustment of fall detection parameters either automatically or through a user interface.

A falling detection method and an electronic system using the same are provided. The falling detection method includes: obtaining a video stream, and performing recognition on a person in an image of the video stream to obtain at least one human body feature; calculating based on the human body feature to obtain at least one falling related feature; calculating at least one movement trend in a plurality of directions of the person in the video stream by using an optical flow algorithm; inputting the falling related feature and the movement trend to a falling classifier, such that the falling classifier generates an output result indicating whether a falling event happens to the person; and sending an alarm according to the output result.

The invention relates to a fall detection method based on a video. The method comprises steps that S1, video images are processed, and human body areas of the images are identified and positioned; S2, articulation point extraction based on the cascade regression network is carried out for the human body areas to acquire a set of human body articulation points, the first stage network is cascaded back with multiple regression networks having identical structures so as to precisely adjusting coordinate positions of the human body articulation points; and S3, a motion vector of each human body articulation point is taken as a human body motion characteristic, through analyzing articulation point change, whether a human body falls is dynamically analyzed. The method is advantaged in that whether the elderly people living alone falls can be accurately and conveniently determined through video image analysis.

A wearable health monitoring device includes at least one sensor for obtaining a signal indicative of movement of a user in at least one axis and logic configured to determine, based upon the signal, whether the user has fallen.

The invention relates to a fall detection system, for detecting human fall incidents. The detection system comprises a sensor module to be worn by a user, for collecting motion data of his body, for instance acceleration data; first evaluation logic for identifying a potential fall incident on the basis of an impact observed in the acceleration data; and second evaluation logic for verifying that the sensor module is actually worn by the user, at least around the time of said impact, to prevent the system from raising an alarm if only the sensor module has fallen. The second evaluation logic may comprise free fall detection means for evaluating the acceleration data on characteristics which are typical for a free falling object, such as one or more full rotation and / or a free falling phase, during which the magnitude of the acceleration data is relatively stable and small. Additionally or alternatively the second evaluation logic may comprise body proximity detection means for detecting whether the sensor module is in close proximity to the human body.

A method and system for fall detection using sensor fusion are disclosed. In a first aspect, the method comprises in response to any of first and second acceleration magnitude thresholds being satisfied, determining whether a height difference before and after impact of a fall satisfies a threshold and whether an angle threshold between an acceleration vector and a calibration vector is satisfied. In a second aspect, the system comprises a processing system and an application coupled to the processing system, wherein the application carries out the steps of the method.

A system and method for detecting falls is disclosed herein. A mobile pressure sensor is used to generate a mobile pressure signal and a reference pressure sensor at a reference height above a surface is used to generate a reference pressure signal. A processor in communication with the mobile pressure sensor and the reference pressure sensor is used to perform the steps of: (i) determining a height above the surface of the mobile pressure sensor based on the reference height and the reference pressure signal and the mobile pressure signal; and (ii) if the height is below a threshold height, determining that a fall has occurred.

The present invention relates to a light-weight, small and portable ambulatory sensor for measuring and monitoring a person's physical activity. Based on these measurements and computations, the invented system quantifies the subject's physical activity, quantifies the subject's gait, determines his or her risk of falling, and automatically detects falls. The invention combines the features of portability, high autonomy, and real-time computational capacity. High autonomy is achieved by using only accelerometers, which have low power consumption rates as compared with gyroscope-based systems. Accelerometer measurements, however, contain significant amounts of noise, which must be removed before further analysis. The invention therefore uses novel time-frequency filters to denoise the measurements, and in conjunction with biomechanical models of human movement, perform the requisite computations, which may also be done in real time.

An alarm (35) provided by a fall detection system (2) may be caused by an accidental drop of the system. Therefore prior to issuing the alarm the fall detection system a confirmation is needed that a potential fall originates from a fall detection system that is worn by a user (4). A fall of a fall detection system that is not attached to a user is characterized by the occurrence of one or more full rotations of the system. Said rotations are identified by analyzing the output signal of a magnetometer, and by detecting a periodicity in said output signal. The fall detection system (2) provides the alarm in dependence of an identified absence of at least one full rotation.

Embodiments of the invention relate to spectrally and spatially dissecting accelerometer data from a signal of a motion sensor. Data received from the dissected signal is reduced to statistical averages for selected frequency bands and spatial dimensions. A time segment subject over which the signal was acquired is segmented so that the dissected signal may be evaluated per the segmentation of the time. Change of orientation towards gravity of the sensor is analyzed with respect to a change of orientation from a before impact segment to an after impact segment. The analyzed change in orientation value with respect to a threshold is a factor in determination of a fall of a body to which the sensor is attached.

A system for tracking a location of a resident includes a resident height detection device (e.g., a small pendant, bracelet or other wearable device) and a receiver / dialer. When the system detects that the resident height detection device has remained within a threshold distance of the floor (e.g., 8 to 16 inches) for more than a given period of time (e.g., 15 seconds), an alarm condition signal would then be sent to a receiver / dialer elsewhere in the residence which would then forward an emergency signal to a caretaker or to an emergency operator by way of the receiver / dialer. The resident height detection device may further include an override switch to turn off tracking when the resident intends to be on the floor for an extended period of time.

The invention relates to an old man falling detecting method based on multi-sensor fusion. The method includes the steps of automatically collecting information of sensors, constructing feature vectors of the sensors; conducting fusion to form an overall falling feature vector; completing feature selection; and calling a classifier to complete falling identification. The invention relates to a falling alarm system based on the multi-sensor fusion. The falling alarm system is characterized by comprising a sensor information collecting module, a sensor feature vector construction module, a falling feature vector construction module, a feature selecting module, a falling identification module, a falling identification model learning module, a falling alarming module and a falling file management module. The old man falling detecting method and system have the beneficial effects that the falling detection accuracy is high; falling help calling is in time; the functions are comprehensive; the cost is low; and carrying is convenient.

A system and method is provided for detection of a human body fall event. The fall detection system (100, 200) includes a monitoring unit (102, 202), including a plurality of accelerometers (106, 206), a processor (108, 208) and a wireless transmitter (110, 210). The plurality of accelerometers (106, 206) provide acceleration measurements to the processor (108, 208), the measurements describing the current acceleration of the person wearing the monitoring unit (102, 202) in all directions. The processor (108, 208) receives the acceleration measurements and compares the acceleration measurements to a value range to determine if the wearer is currently experiencing a fall event. The processor (108, 208) generates a signal in response to the detection of a fall event and the transmitter (110, 210) transmits the signal to a remote signal receiver (104, 204). The system and method can further detect non-movement in the wearer of the monitoring unit (102, 202) subsequent to the fall event to detect an unconscious wearer.

There is provided a fall detection system for use in detecting falls by a user, the fall detection system comprising a processing unit configured to determine context information about the user and / or the environment in which the user is located, and to increase the sensitivity of a fall detection algorithm used to detect falls by a user in the event that the determined context information indicates that the user is or may be at an increased risk of falling, the increase in sensitivity occurring while the increased risk of falling is indicated by the determined context information.

The invention provides a human falling detection method based on machine vision. Under the conditions that all detected objects are people in indoor environment and the activities of all people can be captured by a depth camera, the method comprises the following steps of: (1) acquiring an image by using the depth camera; (2) subtracting a background from the image which is acquired, and extracting a foreground from the image; (3) acquiring a foreground contour by using a binary image contour extraction algorithm; (4) performing convolution computation on the foreground contour and Gaussian functions in different scales, mapping a contour image to a curvature scale space (CSS) to form a CSS image, extracting peak points of the CSS image, and thus obtaining a video word bag based on curvature scale characteristics by the peak points in different scales; and (5) training an extreme learning machine classifier by using the video word bag which is obtained. By adoption of the method, under the condition of few training samples, an accurate falling detection result can be obtained within a short training time.

Apparatus for detection of human falls, comprises: an acceleration detector, for detecting vibration events, typically placed on a floor, a microphone, located in association with the acceleration detector for detection of corresponding sound events, and a classification unit to classify concurrent events from the microphone and the acceleration detector, thereby to determine whether a human fall is indicated. If the event appears to be a human fall, then an alarm is raised.

An active hip protector system and method for hip fracture prevention are introduced in order to overcome the short-comings of existing methods of preventing hip fractures; namely: to provide effective impact protection and eliminate false fall alarms. The hip protector system comprises a belt-like pouch, worn over the user's waist, containing airbags which are inflated to a large size ensuring that the user's thighs will not hit the ground upon impact, once the system detects a fall. The pouch contains all of the necessary elements for fall detection and activating inflation of the airbags. Incorporation of distance measurement sensors and cross reference with acceleration and spatial orientation sensors enable reliable detection of impending collision with the ground. The pneumatic system, including the airbags, is carried within the pouch or, alternatively, can be placed in a location that is prone to falling.

There is provided a fall detection system, comprising a user device comprising one or more sensors for measuring the movements of a user; and the system further comprising a processing unit configured to operate in a first mode in which the processing unit processes the measured movements to determine if the user has fallen and a further mode in which the processing unit processes measured movements obtained during a user-specified time period to determine the risk of the user falling.

There is provided a fall detection and / or prevention system, comprising one or more sensors for detecting characteristics of movement of a user of the fall detection and / or prevention system and for generating corresponding signals; processing means for analyzing the signals from the one or more sensors using a fall detection algorithm to determine if a fall has taken place or is likely to take place; wherein the processing means is further adapted to update said fall detection algorithm based on the result of the analysis of the signals and an indication whether a fall has actually taken place from the user or a third party.

A system and method of motion analysis, fall detection, and fall prediction using machine learning and classifiers. A wearable motion sensor for collecting and transmitting motion data for use in a fall prediction model using features and parameters to classify the motion data and notify when a fall is emergent. Using machine learning, the fall prediction model can be created, implemented, evaluated, and it can evolve over time with additional data. The system and method can use individual data or pool data from various individuals for use in fall prediction.

A non-volatile semiconductor memory device includes: a memory cell array, in which electrically rewritable and non-volatile memory cells are arranged: a sense amplifier circuit configured to read data of the memory cell array; and a pass / fail detection circuit configured to detect write or erase completion based on verify-read data stored in the sense amplifier circuit in data write or erase mode, wherein the pass / fall detection circuit comprises a data latch, into which a defective column isolation data is writable in accordance with a command input.