Determination of simple thresholds for accelerometry-based parameters for fall detection

Department of Medical Technology, University of Oulu, Oulu, Finland.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2007; 2007:1367-70. DOI: 10.1109/IEMBS.2007.4352552
Source: PubMed


The increasing population of elderly people is mainly living in a home-dwelling environment and needs applications to support their independency and safety. Falls are one of the major health risks that affect the quality of life among older adults. Body attached accelerometers have been used to detect falls. The placement of the accelerometric sensor as well as the fall detection algorithms are still under investigation. The aim of the present pilot study was to determine acceleration thresholds for fall detection, using triaxial accelerometric measurements at the waist, wrist, and head. Intentional falls (forward, backward, and lateral) and activities of daily living (ADL) were performed by two voluntary subjects. The results showed that measurements from the waist and head have potential to distinguish between falls and ADL. Especially, when the simple threshold-based detection was combined with posture detection after the fall, the sensitivity and specificity of fall detection were up to 100 %. On the contrary, the wrist did not appear to be an optimal site for fall detection.

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Available from: Maarit Kangas
    • "Wearable sensor systems are increasingly being developed for health applications such as activity recognition [1] [2] [3] [4] [5], energy expenditure estimation [6] [7] [8] [9] [10] [11], gait analysis [12] [13], balance assessment [14] and fall detection [15] [16] [17]. The use of accelerometer-based fitness monitors has also exploded recently. "
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    ABSTRACT: This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body–ankle, thigh, hip, arm and wrist–from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively. OPEN ACCESS FULL TEXT AVAILABLE UNTIL September 9th at the following link:
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    • "Literatürdeki düşme sezme ve hareket sınıflandırma çalışmalarında önerilen sistemlerin bir kısmı ivme-ölçerlerden toplanan verilerin analizi ile çalışmaktadır [2] [3]. Ancak bu tür sistemler hızlı oturma veya zıplama gibi aktiviteleri düşmeden ayırt etmede zorlanmakta ve bazı durumlarda yanlış sonuçlar üretmektedir. "
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    • "11 /10.6109/jicce.20 pproaches to au rs or gyroscop Automatic fa d of single p ation measured bout 100%), b atic fall detec s has a relative 90.1%) [11]. cities measured 100%) and sp ll detection us d using the ac an accelerome ensitivity (91% cause the repo desired sensi opment of adva d fall detectors ence and safe applications a poses [6] [7] [8] [9] [10] [11] [12] [13] an en though falls tems, the situ may not cause required to se enter in the c n falls. aper, we prop a sensor node for the elderly, orithm. "
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    ABSTRACT: An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.
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