Power-Efficient Interrupt-Driven Algorithms for Fall Detection and Classification of Activities of Daily Living

Yuan, Jun; Tan, Kok Kiong; Lee, Tong Heng; Koh, Gerald Choon-Huat

doi:10.1109/jsen.2014.2357035

Cited by 55 publications

(7 citation statements)

References 21 publications

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“…Two similar prototypes which are optimized to reduce the consumption are presented in the work by Ren et al [19] (exhibiting an estimated battery lifetime of up to 21.7 days) and by Yuan et al in [20] (with a battery lifespan of 78.7 days). In both cases, the employed low-power technology (802.15.4) of the transceiver restricts the transmission range to some meters so that the operation of the system is limited to home-based environments in which a RF-receiver can be located in the vicinity of the user.…”

Section: Consumption In Wearable Fdss Based On a Specific Device (Notmentioning

confidence: 99%

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Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

González-Cañete

Casilari

2020

Sensors

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Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.

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Section: Consumption In Wearable Fdss Based On a Specific Device (Notmentioning

confidence: 99%

“…In a hybrid FDS, the design of the interaction between the smartphone and the external sensors to generate the detection alarm may follow three different strategies [20].…”

Section: Consumption In Hybrid Fall Detection Architectures (Combininmentioning

confidence: 99%

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

González-Cañete

Casilari

2020

Sensors

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“…Falling accidents that involve elderly people are an important issue in home care [1][2][3][4][5][6][7][8][9][10][11][12][13], because 28~35% of people over 65 years old have falling accidents more than once every year [6,8,10], and 10~20% of those suffer from injuries that involve hospitalization or death [5]. Without assistance, most elderly people cannot raise themselves and so remain on the ground after a fall, leading to serious physiological effects with enormous medical costs [3,7,14].…”

Section: Introductionmentioning

confidence: 99%

“…Without assistance, most elderly people cannot raise themselves and so remain on the ground after a fall, leading to serious physiological effects with enormous medical costs [3,7,14]. To monitor falling accidents, several systems that exploit a variety of methods, such as computer vision and motion sensing, have been proposed [1][2][3][4][5][6][7][8][9][10][11][12][13]. The most common means of fall detection involve placing spatially distributed sensors (including cameras [1,3,7], microphones [2,3], and floor sensors [11,12]) within the elderly person's house to establish a smart home environment, in which a falling accident is identified by image, audio and vibration sensors.…”

Section: Introductionmentioning

confidence: 99%

“…However, this system functions correctly only when the person is in the area where the sensors are mounted with the absence of obstacles in the sensing range. Therefore, several systems have been developed using wearable devices; they have good portability without being limited to a particular region [4][5][6]9,10,13,15]. The accelerometer, gyroscope, and magnetometer are sensors that are commonly integrated into unobtrusive products as wearable devices due to their small size and low power consumption.…”

Section: Introductionmentioning

confidence: 99%

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Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements

Lin

Chiu

Chu

et al. 2020

Sensors

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This work presents a fall detection system that is worn on the head, where the acceleration and posture are stable such that everyday movement can be identified without disturbing the wearer. Falling movements are recognized by comparing the acceleration and orientation of a wearer’s head using prespecified thresholds. The proposed system consists of a triaxial accelerometer, gyroscope, and magnetometer; as such, a Madgwick’s filter is adopted to improve the accuracy of the estimation of orientation. Moreover, with its integrated Wi-Fi module, the proposed system can notify an emergency contact in a timely manner to provide help for the falling person. Based on experimental results concerning falling movements and activities of daily living, the proposed system achieved a sensitivity of 96.67% in fall detection, with a specificity of 98.27%, and, therefore, is suitable for detecting falling movements in daily life.

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Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

Shewell

Medina-Quero

Espinilla

et al. 2016

Int J Communication

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This paper presents a comparison between algorithms (Oriented FAST and Rotated BRIEF (ORB) andAruco) for the detection of fiducial markers placed throughout a smart environment. A series of activities of daily living (ADL) were conducted while monitoring a first-person perspective of the situation; this was achieved through the usage of the Google Glass platform. Fiducial markers were employed, as a means to assist with the detection of specific objects of interest, within the environment. Each marker was assigned unique Identification (ID) and was used to identify the object. Three activities were performed by a participant within the environment. On subsequent trials of the solution, lighting conditions were modified to assess fiducial marker detection rates on a frame-by-frame basis. This paper presents the results from this investigation, detailing performance measure for each object detected under various lighting conditions, motion blur and distance from the objects. An intelligent system was developed to specifically consider distance estimation in order to aid with the filtering out of false interactions. A linear filtering method was applied along with a fuzzy membership function to estimate the degree of user interaction, which assists in removing false positives generated by the occupant. The intelligent system returns an average precision, recall and an F-Measure of 0.99, 0.62 and 0.49, respectively.

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Power-Efficient Interrupt-Driven Algorithms for Fall Detection and Classification of Activities of Daily Living

Cited by 55 publications

References 21 publications

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements

Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

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