A smart fall and activity monitor for telecare applications

Williams, Gareth J.; Doughty, K.; Cameron, K.H.; Bradley, D.A.

doi:10.1109/iembs.1998.747074

Cited by 109 publications

(62 citation statements)

References 5 publications

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“…3b) The sub-tasks of clutter detection contain floor selection, For the dramatic lighting changes such as turning on/off a background subtraction including background image update, lamp, the background model is also updated when the differences before the thresholding are similar all over the the previous frame. (4), and the closer to 1 its value is, the closer is the similarity of the vectors. The threshold value to classify human and non-human is defined The calculation of the motion and position information of a or climbs furniture, the shortest distance between the point toddler is done by joining corresponding regions, which are and the boundary of the floor area detected previously is classified as human, in successive frames.…”

Section: After Background Subtraction Human and Non-humanmentioning

confidence: 99%

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A Smart Vision Sensor for Detecting Risk Factors of a Toddler's Fall in a Home Environment

Na¹,

Qin²,

Wright³

2007

2007 IEEE International Conference on Networking, Sensing and Control

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Abstract-This paper presents a smart vision sensor for environmental hazards (such as slippery surfaces) and detecting risk factors of a toddler's fall in an indoor home perilous activities (such as inattentive walking) [11]. As environment assisting parents' supervision to prevent fall intrinsic factors are associated with health problems, a normal injuries. We identified the risk factors by analyzing real fall toddler's fall would be based on the extrinsic factors which injury stories and referring to a related organization's include their environments and activities. Therefore, this suggestions to prevent falls. In order to detect the risk factors injuries when they fall. As young children are not able to assess risks for 3) ensure that there is no furniture around available for them themselves, the best way to prevent their falls would be to climb on. continuous supervision and instruction from their parents.Based on the above studies, the risk factors to recognize by However, this is not always practical. A smart vision sensor is our sensor were identified as follows: proposed in this paper to assist the parents' supervision for 1) check if clutter has appeared on the floor. preventing fall injuries. 2) check if a toddler moves around when clutter is presentMany applications have been developed to detect on the floor. potentially fatal falls of the elderly [3][4][5][6][7][8][9][10]. Acceleration 3) check if a toddler moves too close to any structure in sensors worn by users and cameras were used in these cases their environment.for detecting falls. Although some of them collected the fall 4) check if a toddler climbs any furniture. data from the sensors to evaluate the user's personal fall risksThe first factor was defined for places such as a corridor for later prevention, there was no prevention against falls where clutter should not be on the floor and could even trip during the data collection and also against irregular falls adults. The second factor was defined for the case that a afterwards. Some wearable devices provided prompt toddler is playing with toys on the floor of a play room. If a protection such as an airbag and an overhead tether when toddler sits still while playing with the toys, the toys do not sensing a fall, but required the user to wear these all the time.

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Section: After Background Subtraction Human and Non-humanmentioning

confidence: 99%

“…potentially fatal falls of the elderly [3][4][5][6][7][8][9][10]. Acceleration 3) check if a toddler moves too close to any structure in sensors worn by users and cameras were used in these cases their environment.…”

Section: ) Check If a Toddler Moves Around When Clutter Is Presentmentioning

confidence: 99%

A Smart Vision Sensor for Detecting Risk Factors of a Toddler's Fall in a Home Environment

Na¹,

Qin²,

Wright³

2007

2007 IEEE International Conference on Networking, Sensing and Control

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“…Multiple sensors with accelerometers placed at various bodily sites are used for real time human movement detection [6,7,8]. Many systems [2,9,10,11,12] employ triaxial accelerometers to detect fall according to the acceleration of body motion and posture angle. To achieve better accuracy, later systems [13,14,15] detect fall combining accelerometer with barometric pressure sensor, image process, and gyroscope etc.…”

Section: Introdutionmentioning

confidence: 99%

HONEY: A Multimodality Fall Detection and Telecare System

Zhang

Ren

Shi

2013

Telemedicine and e-Health

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ABSTRACT-The increasing cost of finance and healthcare resources is driving healthcare providers to provide home-based telecare instead of institutionalized healthcare. Falling is one of the most common and dangerous accidents for elderly group and a significant factor affecting the living quality of the elderly. Many efforts have been put towards providing a robust method to detect falls accurately and timely. To facilitate a reliable, safe and real-time home-based healthcare environment, we propose the HONEY system to detect falls for elderly people in the home telecare environment. The basic idea of HONEY is a three-step detection scheme which consists of multimodality signal sources, including an accelerometer sensor, audio, images and video clips via speech recognition and on-demand video techniques. The magnitude acceleration, corresponding to a user's movements, triggers fall detection combining speech recognition and on-demand video. If a fall occurs, an alarm email is delivered to a medical staff or caregivers at once, containing the fall information so that caregivers could make a primary diagnosis based on it. This paper also describes the implementation of the prototype of HONEY. A comprehensive evaluation with 10 volunteers shows that HONEY has high accuracy of 94% for fall detection, 18% higher than Advanced Magnitude Algorithm (AMA), which is a wearable sensor-based method, while the false positive rate and false negative rate are 3% and 10%, 19% and 16% lower than AMA, respectively. The average response time for a detected fall is 46.2 seconds, which is also short enough for first aid.Index Terms-Fall detection, Health information management, Home telecare, Medical information systems I. INTRODUTIONThe impending influx of elderly citizens aged over 65 will soon be a much larger ratio to all population around the world. For example, in 15.07% of 23 million population of Shanghai is more than 60 years old [1]. Falling is a common and dangerous accident for elderly group and an important factor affecting the living quality of the elderly. It is estimated that more than one in three elder people aged over 65 living at home fall at least once each year. The risk of falling will also rise with the increasing age. Falling also leads to decreased mobility, fear of fall, and even deaths [2,3,4]. Furthermore, a research among Medicare (the largest healthcare insurer in USA) beneficiaries shows that the total medical care costs each year for elderly adults who fall once a year, are 29 % higher than the ones for other elderly adults who never fall in one year. Similarly, the costs for elderly adults who fall more than two times a year are 79 % higher than ones for those who report no fall [5]. However, the limited funds for public healthcare service and an aging population are driving factors for reducing the institutionalized healthcare based on entity service and moving to home healthcare based on wireless sensor networks.Accelerometers with low-cost and low-power features make the wearable and reliable fa...

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“…The main reasons elderly people become bedridden are apoplectic ictus, decrepitude, falls, and fractures [4]. Fall-related injuries have also been among the five most common causes of death amongst the elderly population [5]. The early detections and recording of fall incidents can help the elderly to obtain in-time medical treatments as well as help identify reasons of incidents while sustaining a fall.…”

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confidence: 99%

Automatic Fall Incident Detection in Compressed Video for Intelligent Homecare

Lin

Ling

2007

2007 16th International Conference on Computer Communications and Networks

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Abstract-This paper presents a compressed-domain fall incident detection scheme for intelligent homecare applications. First, a compressed-domain object segmentation scheme is performed to extract moving objects based on global motion estimation and local motion clustering. After detecting the moving objects, three compressed-domain features of each object are then extracted for identifying and locating fall incidents. The proposed system can differentiate fall-down from squatting by taking into account the event duration. Our experiments show that the proposed method can correctly detect fall incidents in real time.

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A smart fall and activity monitor for telecare applications

Cited by 109 publications

References 5 publications

A Smart Vision Sensor for Detecting Risk Factors of a Toddler's Fall in a Home Environment

A Smart Vision Sensor for Detecting Risk Factors of a Toddler's Fall in a Home Environment

HONEY: A Multimodality Fall Detection and Telecare System

Automatic Fall Incident Detection in Compressed Video for Intelligent Homecare

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