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In the third experiment we validate the thresholds found in the second experiment with accelerometer data that contain values of fall. For that we put the accelerometer at the height of the pocket of pants, and throw it on the floor for 6 times and between each time we performed different activities such as stand, sit, walk. We collected the accelerometer data during 2 minutes at a frequency of one sample per half second for a total of 239 samples. We calculated the mean of the module and then we created a function that detect the falls in the same way that the Esper Rule, with the Fall Threshold. | In the third experiment we validate the thresholds found in the second experiment with accelerometer data that contain values of fall. For that we put the accelerometer at the height of the pocket of pants, and throw it on the floor for 6 times and between each time we performed different activities such as stand, sit, walk. We collected the accelerometer data during 2 minutes at a frequency of one sample per half second for a total of 239 samples. We calculated the mean of the module and then we created a function that detect the falls in the same way that the Esper Rule, with the Fall Threshold. | ||
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=== Formalization === | === Formalization === | ||
- | · | + | * Objective: validate the thresholds found in the second experiment with accelerometer data that contain values of fall, creating a function fall(x) that detect the falls in the same way that the Esper Rule. |
- | · | + | * Set-up: accelerometer at the height of the pocket of pants, and throw it on the floor for 6 times and between each fall we performed different activities such as stand, sit, walk. We collected the accelerometer data during 2 minutes at a frequency of one sample per half second for a total of 239 samples. |
- | · | + | * Parameters to be varied: activities performed: none, walk, sit, stand and fall |
- | · | + | * Metrics: |
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- | Where {{pmu%20relatorio%202_files: | + | Where {{image028.png? |
- | + | ||
- | · Results: As result, all the 6 times the function detects the fall. | + | |
+ | * Results: As result, all the 6 times the function detects the fall. | ||
===== 4.2 Test of thresholds ===== | ===== 4.2 Test of thresholds ===== | ||
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{{image032.png? | {{image032.png? | ||
- | }====== 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)/ | 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)/ | ||
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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. | ||
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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: | ||
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**[5] A. K. Bourke, J. V O’Brien, and G. M. Lyons, “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.”, | **[5] A. K. Bourke, J. V O’Brien, and G. M. Lyons, “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.”, | ||
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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 ====== |