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falldetectionsystem [2014/12/09 10:11]
mroriz [4.1.3 Experiment 3]
falldetectionsystem [2017/07/21 06:08] (current)
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-}====== 5. Related Work  ======+} 
 +====== 5. Related Work  ======
  
 We read various papers about fall detection by processing accelerometer data. Here we present a summary of each one. We read various papers about fall detection by processing accelerometer data. Here we present a summary of each one.
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 They detect various fall stages: They detect various fall stages:
  
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 This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Fall detection and fall prevention systems have the same basic architecture. Both systems follow three common phases of operation: sense, analysis and communication. The basic difference between the two systems lies in their analysis phase with differences in their feature extraction and classification algorithms. Fall detection systems try to detect the occurrence of fall events accurately by extracting the features from the acquired output signal(s)/​data of the sensor(s) and then identifying fall events from other activities of daily living (ADL). On the other hand, fall prevention systems attempt to predict fall events early by analyzing the outputs of the sensors. Data/signal acquisition,​ feature extraction and classification,​ and communication for notification are the necessary steps needed for both fall detection and prevention systems. The number and type of sensors and notification techniques however, vary from system to system. This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Fall detection and fall prevention systems have the same basic architecture. Both systems follow three common phases of operation: sense, analysis and communication. The basic difference between the two systems lies in their analysis phase with differences in their feature extraction and classification algorithms. Fall detection systems try to detect the occurrence of fall events accurately by extracting the features from the acquired output signal(s)/​data of the sensor(s) and then identifying fall events from other activities of daily living (ADL). On the other hand, fall prevention systems attempt to predict fall events early by analyzing the outputs of the sensors. Data/signal acquisition,​ feature extraction and classification,​ and communication for notification are the necessary steps needed for both fall detection and prevention systems. The number and type of sensors and notification techniques however, vary from system to system.
  
-{{pmu%20relatorio%202_files:​image040.png?​567x166}}Most solutions employ the tri-axial accelerometer for sensing which measure simultaneous accelerations in three orthogonal directions. Threshold-based algorithms use these acceleration values for calculating Signal Magnitude Vector by using the following relation:+{{image040.png?​567x166}}Most solutions employ the tri-axial accelerometer for sensing which measure simultaneous accelerations in three orthogonal directions. Threshold-based algorithms use these acceleration values for calculating Signal Magnitude Vector by using the following relation:
  
-{{pmu%20relatorio%202_files:​image042.png?​335x42}}Where Ax, Ay, and Az represent tri-axial accelerometer signals of the x, y, and z-axis respectively. If the value of signal magnitude vector for a particular incident exceeds a predefined threshold value, then the algorithm primarily identifies that incident as a fall event.+{{image042.png?​335x42}}Where Ax, Ay, and Az represent tri-axial accelerometer signals of the x, y, and z-axis respectively. If the value of signal magnitude vector for a particular incident exceeds a predefined threshold value, then the algorithm primarily identifies that incident as a fall event.
  
 Whenever a SP-based solution detects or predicts a fall event, it communicates with the user of the system and/or caregivers. Most fall detection solutions carry out this communication phase in two steps. In the first step, the system attempts to obtain feedback from the user by verifying the preliminary decision and thus improve the sensitivity of the system. The second step depends on the user’s response. If the user actively rejects the suspected fall, then the system restarts. Otherwise, a notification is sent to caregivers to ask for immediate assistance. Some systems may not wait for user’s feedback and will immediately convey an alert message to the caregiver. User’s feedback can be collected automatically by analyzing the sensor’s output for example automatically analyzing the difference in position-data before and after the suspected fall event. Other systems demand manual feedback from the user. Whenever a SP-based solution detects or predicts a fall event, it communicates with the user of the system and/or caregivers. Most fall detection solutions carry out this communication phase in two steps. In the first step, the system attempts to obtain feedback from the user by verifying the preliminary decision and thus improve the sensitivity of the system. The second step depends on the user’s response. If the user actively rejects the suspected fall, then the system restarts. Otherwise, a notification is sent to caregivers to ask for immediate assistance. Some systems may not wait for user’s feedback and will immediately convey an alert message to the caregiver. User’s feedback can be collected automatically by analyzing the sensor’s output for example automatically analyzing the difference in position-data before and after the suspected fall event. Other systems demand manual feedback from the user.
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 Smartphone-based solutions can also be categorized on the basis of algorithms used in the analysis phase. Smartphone-based solutions can also be categorized on the basis of algorithms used in the analysis phase.
  
-{{pmu%20relatorio%202_files:​image044.png?​260x153}}Existing and potential SP-based fall detection and prevention systems communicate with the users, caregivers or assistive systems by sending alert signals, obtaining user or system feedback or activating assistive systems.+{{image044.png?​260x153}}Existing and potential SP-based fall detection and prevention systems communicate with the users, caregivers or assistive systems by sending alert signals, obtaining user or system feedback or activating assistive systems.
  
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 One of the algorithms uses the magnitude Mi of the acceleration vector at each ith sample: One of the algorithms uses the magnitude Mi of the acceleration vector at each ith sample:
  
-{{pmu%20relatorio%202_files:​image048.png?​167x27}}Where xi, yi and zi are the accelerations in the respective axes. A fall is suspected if a lower (TH1a) and an upper (TH1b) threshold are crossed in a given short duration of time (W1a). These thresholds are adjusted based on user age, weight, height and level of activity. After this phase, the algorithm checks if the orientation2 changed (TH1c) with respect to the last orientation,​ recorded while the phone was resting at M= 1 g for a long period of time (W1b), before the threshold crossing. Finally, if the changed position remains constant (TH1d) for another given time window (W1c), in order to account for the fact that the person may stand up again, then a fall is detected.+{{image048.png?​167x27}}Where xi, yi and zi are the accelerations in the respective axes. A fall is suspected if a lower (TH1a) and an upper (TH1b) threshold are crossed in a given short duration of time (W1a). These thresholds are adjusted based on user age, weight, height and level of activity. After this phase, the algorithm checks if the orientation2 changed (TH1c) with respect to the last orientation,​ recorded while the phone was resting at M= 1 g for a long period of time (W1b), before the threshold crossing. Finally, if the changed position remains constant (TH1d) for another given time window (W1c), in order to account for the fact that the person may stand up again, then a fall is detected.
  
 **[5] A. K. Bourke, J. V O’Brien, and G. M. Lyons, “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.”,​ Gait Posture, vol. 26, no. 2, pp. 194--9, Jul. 2007.** **[5] A. K. Bourke, J. V O’Brien, and G. M. Lyons, “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.”,​ Gait Posture, vol. 26, no. 2, pp. 194--9, Jul. 2007.**
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 -          Lower fall threshold: negative peaks for the resultant for each recorded activity are referred to as the signal lower peak values (LPVs). The lower fall thresholds (LFT) for the trunk and thigh signals were set at the level of the smallest magnitude lower fall peak (LFP) recorded for the trunk and thigh resultant vector signals. These levels of LFT would thus result in 100% detection of the 240 falls recorded for each of the resultant vector signal thresholds individually. The LFT is related to the acceleration of the trunk at or before the initial contact of the body segment with the ground. -          Lower fall threshold: negative peaks for the resultant for each recorded activity are referred to as the signal lower peak values (LPVs). The lower fall thresholds (LFT) for the trunk and thigh signals were set at the level of the smallest magnitude lower fall peak (LFP) recorded for the trunk and thigh resultant vector signals. These levels of LFT would thus result in 100% detection of the 240 falls recorded for each of the resultant vector signal thresholds individually. The LFT is related to the acceleration of the trunk at or before the initial contact of the body segment with the ground.
  
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 ====== 6. Conclusion ====== ====== 6. Conclusion ======
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