life warning fall detection

UNDERSTANDING FALLS HEALTH CONSEQUENCES

Fall Consequences

A serious consequence of a fall is a “long – lie”, an extended period of me where the vicm remains immobile on the ground. The “long – lie” occurs in more than 20% of elderly admied to the hospital due to ffalling and can lead to hypothermia, dehydra-on, broncho pneumonia, and pressure sores (Masud & Morris, 2001). Previous works in the field of elderly populaon fall circumstances found that 82% off falls occurred from a standing height, and that a forward fall is the most common falltype (O'Neill, et al., 1994), (Vellas, Wayne, Garry, & Baumgartner, 1998). Those findings may indicate that falls mostly occurred during walking. Sideways (lateral) falls are less common but can cause a lateral force that may fracture an elderly persons hip.

The Internaonal Classificaon of Disease–9 defines a fall as; ‘a fall is an unexpectedevent where a person falls to the ground from an upper level or the same level’.According to the Center of Disease Control and Prevenon (CDC) one out of three adultsaage 65 and older falls each year. Falls are the leading cause of injury related death,non-fatal injuries and trauma hospitalizaon among people 65 and older (Masud &Morris, 2001). Injuries sustained from falls include broken bones, superficial cuts andababrasions to the skin as well as connecve and so ssue damage. Fall related admissions of older adults are a significant financial burden to the health services worldwide. The CDC eesmates that in 2000, direct medical costs of falls totaled a lile over $19 billion—$179 mil-lion for fatal falls and $19 billion for nonfatal fall injuries (Center of Disease Control and Prevenon, 2010).

FALL DETECTION

This literature reviews present Real---Time fall detecon algorithms designed for data input from one wireless wearable sensor node, which may include more than one sensor. A subsequent pre-sentaon will provide a summary of researched fall characteriscs, and the locaon of the node on the subject’s body is discussed.

Exisng fall detecon methods can be divided into two main categories, based on a wearable device or environment based. A wearable device is using moon---tracking sensor like accelerom-eters and gyroscopes, and environmental methods use sensors in the subjects’ surrounding such as video, audio, and vibraon signal. The majority of the academic work on wearable fall detec-on device the research was based on accelerometers. An Australian group (Boyle & Karunanithi, 2008) developed a movement classificaon algorithm based on a bi---axial accelerometer and its change rate and was able to detect forward and backward falls. Chen et al. (Chen, Kwong, Chang, Luk, & BajcLuk, & Bajcsy, 2005) presented a device containing a 3D accelerometer worn on the wrist. The al-gorithm proposed is acceleraon threshold and orientaon based.

When a threshold value is crossed a fall is suspected. Then the change in orientaon derived from the acceleraon vector prior and post to the event. The orientaon is esmated over a one second signal prior and post to the event it is assume that the orientaon is not changing during that me, which may not be the case for dynamic situaons like running or even walking. Wang et al. (Wang, et al., 2008) pleased a 3D accelerometer above the ear. The algorithm ulized the total acceleraon and the sum of frontal and sagial acceleraon component (the horizontal plain). It also took into account the total velocity (calculated using acceleraon integraon). This sese ng was able to detect 100% of the tested events.

Kangas et al. (Kangas, Kon la, Winblad, & Jamsa, 2007) suggested several parameters for thresh-old algorithms. The authors divided the measured acceleraon to dynamic and stac compo-nents. The two components were derived from the 3D acceleraon signal. The stac component was used in posture analyses and the dynamic data in moon analyses and vercal acceleraon calculaon. Bourke et al. (Bourke, O’Donovan, & ÓLaighin, The idenficaon of vercal velocity profiles using an ineral sensor to invesgate pre---impact detecon of falls, 2008) also re-searched the properes of vercal velocity for fall detecon and found it to be a predicve fea-tuture; that was able to detect the fall 323ms prior to impact. Bourke and Kangas compared fall de-tecon algorithms based on features extracted from 3D acceleraon signal. The features targeted different fall characteriscs as impact, velocity, and posture. The algorithms used those character-iscs with increasing complexity, first only the impact was used, then impact and posture and so on. Both authors found simpler algorithms to be more accurate.

FALL DETECTIONALGORITHMS

A fall starts with a short free fall where the acceleraon should decries bellow 1G. When the suject hits the ground the acceleraon increases, above 3G (Sposaro & Tyson, 2009). Immediately aer the impact there is an aer shock, evident in fast fluctuaons of the acceleraon signal. If there is a serious injury the subject will stay on the ground and the acceleraon should be close to 1G. Furthermore body posion should change by approximately 90°, when moving from the up-right to the horizontal posion. Typical fall duraon is approximately 1.5 seconds (Chen, Kwong, Chang, Luk, & Bajcsy, 2005). The below graph shows a typical fall accelerometer reading.

In order to detect human falls an accelerometer, magnetometer, gyroscope, and a microphone were used. Data from Falls, Nearly Falls and ADLs were recorded from a single sensor node. The signals were processed and relevant features were extracted from each event. Gaussian mixture model (GMM) classifier was implemented on a subset of a selected features space, selected via a SFFS algorithm designed to maximize the classifier performance. Each class, Fall and ADL (the Nearly falls were labeled as ADLs for the purpose of classificaon), was represented by a GMM trained using an expectaon maimizaon algorithm. When a new event is recorded, the signal un-dedergoes processing, feature extracon and classificaon according to its compability to the pre-viously trained models.

Classificaon System

Fall Charcteriscs

ALGORITHMSFALL DETECTION

Any other works ulize addional sensors, Sposaro and Tyson (Sposaro & Tyson, 2009) and Dai et al. (Dai, Bai, Yang, Shen, & Xuan, 2010) developed fall detecon smart phone applicaon using both its acceleraon and orientaon sensors. There are very few methods proposed in the litera-ture that ulizes the acousc properes of a fall. Most of the related work focuses on collecng and analyzing sound captured from the subject’s environment. Grassi et al. (Grassi, et al., 2008) developed a prototype using three sensors, a 3D camera, a wearable accelerometer, and a staon-ary microphone. Each sensor detecng fall events separately and in the future the results meant tto be fused. The audio signal were used to train an HMM network based on Perceptual Linear Pre-dicon (PLP) features. However the classificaon results were very poor, 60% reliability was achieved. It is very difficult to isolate sounds in close proximity of the subject using microphones.

A Greek group is the only one to use a wearable microphone for fall detecon (Doukas & Maglogi-annis, 2008). The authors suggested a wearable system containing a 3Daccelerometer and a microphone, worn on the foot. Two young volunteers preformed a set of ADL and falls with back-ground noise of falling objects and radio. A short me fourier transform was applied for frequency analysis. A human fall generates low frequency <200Hz, and high---energy sound, those properes allowed falls to be correctly classified in 80% of the mes using audio signal alone. The authors also presented a fusion of the two sensor data using a Support Vector Machine (SVM) classifier.

There are great number of works in the field of fall detecon using mulple sensor nodes in different body locaons. For instance (Li, Stankovic, Hanson, Barth, & Lach,2009) used two accel-erometers and gyroscopes, one on the chest and another on the thigh. This can improve detecon for challenging fall events like vercal falls, however impraccal due to the need to aach several devices to the body.

Another approach is to place sensors in the subjects’ environment; those methods ulize video, audio or vibraon signals recorded from staonary sensors. A GMM based classificaon system susuggested by Zigel et al. (Zigel, Litvak, & Gannot, 2009) used vibraon and audio signals to achieve highly accurate results (97.5% sensivity and 98.6% specificity). Zhuang et al. (Zhuang, Huang, Potamianos, & Hasegawa---Johnson, 2009) presented a high complexity classificaon system based on GMM supervectors. The system used merely on staonary microphone and therefore achieved only 67% detecon rate. Such systems and methods require one or more sensors to be distributed in several locaons on or in the vicinity of the user, which is not convenient for the user nor is it praccal to implement when the user is in an unfamiliar environment. Moreover, the classificlassificaon methods employed hereinabove typically require connuous monitoring and classifi-caon, thereby consuming large amounts of processing and electrical power.


Life Warning’s device ulizes one sensor node wearable device containing an accelerometer, mag-netometer, gyroscope and microphone. In order to save power the device connuously samples the acceleraon alone and runs a triggering algorithm to detect suspicious events. If such an event is detected the sensors samples are recorded and transmied to the cloud for addional processing. A classificaon decision is then communicated back to the device. The suggested system uses only one mobile device that can be worn in mulple locaons and orientaons.

In oIn order to detect human falls an accelerometer, magnetometer, gyroscope, and a microphone are used. All data from falls, near falls and ADLs are recorded from a single sensor node. The signals asignals are processed and relevant data are then extracted from each event. Selected feature space, selected via a SFFS algorithm designed to maximize the classifier performance. Each class, Fall and ADL (the near falls are labeled as ADLs for the purpose of classificaon), represented by a GMM trained using an expectaon maximizaon algorithm. When a new event is recorded, the signal undergoes processing, feature extracon and classificaon according to its compability to the previously trained models.

The following table summarizes all the features (fall characteriscs) extracted from the relevant sensors described during the fall detecon algorithm review.

FALL DETECTION

This list of features can be used as a basis for a threshold-base and a learning fall deteconalgorithm. An automac feature selecon algorithm can be used to determine the most signifi-cant features for differenang falls from ADL.

SENSOR LOCATION

DATABASEData was collected from 14 subject stasc is presented in Table 2. Each volunteer performed predetermined ADLs, falls and nearly falls a total of 1670 falls, 480 Nearly falls, and 1005 fall-resembling ADL were collected, a complete count of the falls, nearly falls and ADL types is shown in Table 3 (A much larger number of ADLs and Nearly Falls was collected, however they did not cross the inial triggering algorithm, and therefore will not be taken in to account as valid training data).

The locaon of the sensor node affects the signals recorded. For instance if a sensor is placed onthe extremies (arms or legs) the variaon in movement (acceleraon and rotaon) will be greaterthan if a sensor was put on the trunk or more so on the head (Wang, et al., 2008). Bourke et al.(Bourke & Lyons, A Threshold-Based Fall- Detecon Algorithm using a Bi-Axial GyroscopeSensor, 2008) placed the same sensor on the trunk and thigh and found the trunk sensor baseddetecon to be more reliable. Dai et al. (Dai, Bai, Yang, Shen, & Xuan, 2010) tested threelolocaons, chest, waist and thigh. They found the waist to be the most reliable, achieving deteconrate of 2.67% false negave, and 8.7 false posive. The threshold applied for the fall deteconalgorithm may have to be different for different sensor posions.

FALL DETECTION

Table 3: The Number of Different Falls, Nearly Falls and ADL Types in the Data Base.

FALL DETECTION

We suggest an adapve paern recognion system for fall detecon. The algorithm is based ondata collected from four sensors, accelerometer, gyroscope, magnetometer and a microphone.Figure 3 presents the algorithm schemacs, and the following secon explains each step.

Figure 3: Fall detecon algorithm

TRIGGERING ALGORITHMSSUGGESTED ALGORITHS


During this step the sampled acceleraon is smoothed using a first order Low-pass with 7Hzcutoff frequency

Pre – Processing

Total Acceleraon

Sampling TriggerThe proposed system is constantly working in a low power consumpon mode and sampling accelesampling acceleraon at 125Hz. When the system is triggered the microphone, gyroscope and magnetometer sampling is iniated. Audio signal must be sampled at a much higher rate than the accelerometer and gyroscopes are expensive power-wise, and therefore not connually sampled. In order to use the angular velocity, magnec field and audio data for fall detecon we must record the signals of the impact, meaning the trigger needs to be acvated before the impact, as early as possible aer a fall onset. The first phase of a fall is a short “free” fall where the stac acceleraon (1G) decreases and as a result the result the total acceleraon (a tot) falls below 1G. Bourke(Bourke, O'Brien, & Lyons, Evaluaon of a Threshold-Based Tri-Axial Accelerometer Fall Detecon Algorithm, 2007) showed that using a threshold of 0.41G on atot 91.25% specificity could be achieved. Because this threshold (ThL) is the trigger for all addional sampling not one fall can be missed, therefore we suggest using a more sensive signal the vercal acceleraon, avert, adjusted ThL to have no false negave in the price of many false posives. The threshold ThL is set to be 0.64G; if this threshold is crossed the sampling of the gyroscope, magnetometer and microphone is iniated.

Now the vercal acceleraon is calculated as follows

Where acceleraon is a three-dimensional vector of the pre-processed acceleraon, gravity is the three-dimensional gravity vector, ax, ay, and az and gx gy, and gz are the acceleraon and gravity values on each axes.


Aer an event is triggered sampling of the gyroscope, magnetometer and microphone is iniated.The gyroscope and magnetometer are sampled at 125Hz, and audio is sampled at 8kHz. Aninterval of 2 or 7 seconds is recorded depending on the crossing of a second trigger.

Following the free fall phase the subject hits the ground and a deceleraon phase begins,displayed in atot (equaon 3) as a sharp peak, simultaneously a substanal change in orientaon(measured by the lt angle, equaon 4) is noceable due to the body rotaon. In some occa-sions the orientaon change can appear without a prominent posive acceleraon peak. There-fore following the cross of ThL, a fall is suspected, if atot exceeds a second threshold, ThH1/2, and the change in lt angle is respecvely grater then ThTilt1/2.

We found that two consecuve samples of atot greater than the threshold, ThH1/2, of 2.3G/ 1.55G, and two consecuve samples of lt angle change greater then ThTilt1/2, 35deg/ 70deg oc-curring within 2 seconds from the sampling trigger is a good predictor for a fall event. Similarly Kangas et al. (Kangas, Kon la, Winblad, & Jamsa, 2007) found, based on simulated falls data that a threshold of 2G sufficiently separates between falls and ADL.

If a fall is suspected the accelerometer, gyroscope, magnetometer and microphone outputs are sent to the server for further processing.

Triggering Algorithm Performance

According to the table above the algorithm sends 98% of all tested falls and 52.4% of all testedADL’s to the cloud for further classificaon. The test environment that may produces a controlledfall that will transfer to a soer impact compared to an uncontrolled one could explain the 2% ofmissed falls.

Table 4: Triggering Algorithm Performance.

Suspicious event

Tri – axial Gyroscope, Magnetometer and Microphone Sampling


Previous studies by Bourke and (Bourke, et al., 2010), Kangas (Kangas, Kon la, Lindgren,Winblad, & Jamsa, 2008) compared fall detecon algorithms based on impact, velocity, andposture characteriscs. The algorithms used those characteriscs with increasing complexity,first only the impact was used, then impact and posture and so on. Both authors found simpleralgorithms to be more accurate. The authors were adding more and more condions for a fall tobe detected. This can be seen as a mul dimensional plain threshold on the feature space.

Not all the Not all the falls are the same, duraon, velocity, and acceleraon profiles can vary significantly.Backward fall is faster than a forward one, there are falls that don’t evolve trunk angle changelike falls that end with a sing posion (vercal fall). Moreover an ADL can present similarpeak values as a fall. Figure 4 present several examples of fall profiles. Figure 4 A shows thevercal velocity and acceleraon profiles of different type falls, we can clearly see that a sidewayfall is characterized by lower peak values. A vercal velocity of a forward and sideway falls arealso shown is Figure 4 C, not only that they are disncvely different from one another, but ifccompared to Figure 4 A we can see that there is a variaon in profiles for the same type of fall.Figure 4 B shows the differences in angular velocity for forward and backward falls. Changes insensor locaon will also result in different paerns, different peak values for both ADL and falls.An example of acceleraon profiles for a forward fall recorded from a sensor located on the waistand wrist is shown in Figure 5.

We suggest an adapve classificaon system based Gaussian Mixture Model (GMM). Insteadof trying to divide the feature space to fall or ADL connues regions (with a muldimensionalplain) plain) we train a model were the two classes could overlap, and be represented as a combinaonof various falls and ADL types.

CLASSIFICATION EVENT


Figure 4: Different fall profiles. A – (a-d) show vercal acceleraon and velocity profile for forward, backward, sideways and straight dawn falls. B – (a-b) show angular velocity profile for

forward and backward falls. C – (a-b) show vercal acceleraon profile for forward and backward falls.

Figure 5: Total and vercal acceleraon profiles recorded from a sensor on the waist and wrist during a forward fall, based on (Kangas, Kon la, Lindgren, Winblad, & Jamsa, 2008).


The two models are adaptable; their parameters can be changed remotely based on new trainingdata.

When a new event is introduced to the system a compability score is calculated for each classand a classificaon decisions is made based on a predetermined threshold. The suspected eventis classified as an ADL, a fall or an inconclusive event. If a fall is detected the call center iscontacted.

In order to detect human falls an accelerometer, magnetometer, gyroscope, and a microphonewere used. Data from Falls, Nearly Falls and ADLs were recorded from a single sensor node. Thesignals were processed and relevant features were extracted from each event. Gaussian mixturemodel (GMM) classifier was implemented on a subset of a selected features space, selected via a SFFS algorithm designed to maximize the classifier performance. Each class, Fall and ADL (theNearly falls were labeled as ADLs for the purpose of classificaon), was represented by a GMMttrained using an expectaon maximizaon algorithm. When a new event is recorded, the signalundergoes processing, feature extracon and classificaon according to its compability to thepreviously trained models. Figure 6 shows the block diagram of the system.

Classificaon System

Figure 6: Block diagram of the classificaon system.


The purpose of the training stage is to train a model for each group of events, Fall and ADL.The input to the training stage is the signals of all the events from each group. The input signalsundergo pre-processing and feature extracon. Then, a feature subset that best differenatesbetween the groups is selected, and a GMM model for each group is trained. The outputs of thistraining stage are:

• A set of indices, which represents the selected feature subset.• Two GMM models.

These outputs are stored in the memory.

One of our goals is to find the most discriminave features between the Fall, Nearly Fall andADL event groups. A typical fall can be seconed into four stages. The fall starts with a short freefall, following by the subject’s impact with the ground, immediately aer the impact there is anaershock, and if there is a serious injury the subject will remain on the ground for a “long lye”.Furthermore body posion should change by approximately 90°, when moving from the uprightto the horizontal posion, and the microphone should pick up a relavely loud and short noiseaat the me of impact. In order to capture all the characteriscs of a fall four sensors are used,accelerometer, magnetometer, gyroscope, and a microphone. The following features were ex-tracted to represent each stage of a fall and the event dynamic (me between or of stages).

Feature Extracon

Training Phase


FALL DETECTION

Feature selecon procedure aims to obtain the most discriminave features between the twoclasses. The feature selecon is described by two characteriscs, the performance criterion and the selecon procedure.

The performance criterion used in this work is the classificaon error rate esmated using thecross validaon (CV) method (see Appendix A). The classificaon error rate between two classes(C1 and C2) can be represented by a confusion matrix, where b and c are the number of samples misclassified (b is the number of samples from C1 classified as C2 and c is the number of samples from C2 classified as C1), a and d are the number of samples correctly classified.

Featured Selecon

Performance Criterion

The total error rate is (b+c)/number of events, if there is an unequal number of events in each class the class with the greater number of events will influence the error rate, and each cause a bias. Therefore, we used a balanced error rate (BER), which is the average of the classificaon error rates of each class, as given in the following equaon.

Figure 7: Example of a Confusion

Due to the finite number of events the BER is a discrete number (its resoluon is altered by thenumber of events), therefore it was necessary to combine it with connues value. The parametric distance symmetric Kullback Leibler divergence (JD) (Kullback, 1968) was summed with the BER to form the performance criterion.

The selected features minimize the classificaon error esmated using 10 fold CV (see Appendix A).


Sequenal forward floang selecon (SFFS), (Pudil P., 1994), algorithm can be described by threemajor steps, Inclusion, Test, and Exclusion. SFFS begins with the inclusion process to select afeature with best performance. Conducng a test on every feature selected in the same iteraon to-specify features that will reduce the performance follows the inclusion process. If such a feature exists, the SFFS would commence the exclusion process to remove the feature from the selected subset. The algorithm will connue looking for other beer features unl all features are eexamined. The selected features indexes are saved.

Using the selected features, two GMMs were esmated using EM algorithm, which represent the probability density of each group: Fall and . The models are stored in the memory for model matching during the test phase.

In the tesng stage, an unknown event is introduced to the system. The new signal undergoes the per-processing, and the previously selected features are extracted and saved as a feature matrix. Now a score is calculated for the new event. Classificaon decision is based on the com-parison of that score with a threshold determined by at the training phase. Classificaon accura-cy is evaluated by the balanced error rate (BER), an average of classificaon errors for each class.

Several classifiers were tested; Naïve Bayes, and GMM. Naïve Bayes is a simple probabiliscclassifier. It assumes that every feature related to a class is independent of each other. So theprobability of occurrence of a class C, provided the features F1 through Fn is

Experiment Results

Test Phase

Model Training

Sequenal Forward Floang Numbers

The Classifier learns the condional probability of each feature from the training data. Classificaon is performed by calculang the probability of C given the values of features F1 through Fn and then predicng the class with the highest probability value. Though the independence assumpon is far reaching and oen inaccurate in real world data, this method performs surprisingly well for most of the classificaon problems. More details about this method can be found in (Duda R. O.).


Where x is a D-dimensional random observaon vector, are the component densies, andare the mixture weights. Each component density is D-variate Gaussian funcon of the formwith mean and covariance matrix .

The mixture weights have to sasfy the constraint. The complete Gaussian mixture density is pa-rameterized by the mean vector, the covariance matrix and the mixture weight from all compo-nent densies. These parameters are collecvely represented by a model, where

Feature selecon for the different classifiers was conducted. The selecon criterion was basedon the BER, esmated using the CV method (See Feature Selecon secon). Figure 8 shows thebest feature selecon criterion, achieved by a NB classifier with Gaussian distribuon and GMMclassifier with a model of order 37 for the Fall class and GMM of order 47 for the ADL class.

A Gaussian mixture density is a weighted sum of K component densies, as given by the following(Reynolds & Rose, 1995)

Figure 8: Selecon criterion as a funcon of the selected features number.

Figure 8 shows that the GMM classifier surpasses the NB


Table 6 shows the selected feature list.

Table 6: Features Selected by the NB Classifiers


Table 7: Feature selected by the GMM classifiers


Table 7: Feature selected by the GMM classifiers

We can see that the features chosen by both classifiers represent all the stages of a fall pre-im-pact is represented by the number and intensity of acceleraon peaks prior to impact, accelera-on min to max me and the me from threshold crossing to the impact (approximaon of the pre impact stage duraon). The impact is represented by acceleraon and rotaon rate maxi-mums and their duraon, velocity and displacement supremums and the audio analysis of the impact sound. The post impact stage (aer shock) is represented by the number of acceleraon peaks and the rotaon rate variance during that stage. The long lye is represented by its duraon. The overall event is characterized by the overall change in pitch, roll, yaw and lt angles, the overall shape of the acceleraon spectral envelope (AR parameters). The me dynamics of the event is represented by the me passed between significant landmarks in the event such as the me between min and max of the acceleraon. The features chosen using the NB classifier have a similar representaon of the fall stages. However, the NB assumes the features are inde-pendent from each other, which is inaccurate for this problem.


The best classificaon results achieved by each classifier are shown in the table below.

Table 8: Classificaon Results for the NB and GMM Classifiers in the Form of a Confusion Matrix

The best classificaon of 96.5% true fall detecon was achieved by a GMM classifier of 37th order for the Fall class and 46th order for the combined class of the Nearly falls and ADL. The overall performance of the system, the triggering algorithm and the chosen GMM classifier is given below.

Table 9: Overall performance of the fall detecon system

Up unl now, elderly fall detecon algorithms were tested on a simulated fall database performedby young volunteers. A database of ADL and natural falls performed by the target populaon is acurtail for an accurate and reliable fall detecon system. Our system is trained on an experimentalsmulated fall database and adaptable as a database for the target populaon is gained. Figure9 presents the adaptaon process. Every me an event is suspected as a fall, it is saved to thedatabase. If an event is detected as a fall, a call center operator will call the subject to confirm. Inccase of a false alarm, an operator from the call center will indicate that a mistake was made andsave the informaon to the database. At the end of each day, an operator will call to check formissed events. If a person has fallen, the operator will save that informaon to the database. Inthis manner, a large database will be constructed to include falls and ADL, and the system will berestrained and adapted to the complete data set.

FUTURE WORK


Figure 9: System adaptaon process. A fall event is detected by the system, an operator calls toconfirm, the new event is added to the database and the system is restrained.


Figure 10 displays the resubstuon and holdout error esmaons vs. model complexity (modelcomplexity manifests in the number of esmated parameter). The resubstuon and holdout errors are respecvely the lower and higher limits of the true error rate. Cross validaon, and parcularly leave - one – out, is the least bias esmaon and lies between the two limits. The closer the two esmaons the beer the model is fied. Division of the two error esmaons implies over-fi ng (Fukunaga, 1990).

Figure 10: Resubstuon and Holdout Error Esmaon vs. Model Complexity


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longitudinal study of falls in 482 community-dwelling elderly adults. Journal of

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life warning fall detection

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