1. Alan K Bourke1,3 ,Pepijn WJ van de Ven1,
Testing of a long-term fall detection system incorporated into a custom vest for the elderly.
Alan K Bourke1,3 ,Pepijn WJ van de Ven1,
Amy E. Chaya4, Gearóid Ó Laighin2,3, John Nelson1
1. Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
2. Department of Electronic Engineering, National University of Ireland, Galway, Ireland.
3. NCBES, National University of Ireland, Galway, Ireland.
4. University of Pittsburgh, Pittsburgh, U.S.A.

2. Falls and the long-lie
Falls in the elderly are a major international health concern
One in every three community dwelling elderly experience at least one fall every year. (Nevitt et al.,1989,JAMA)
A serious consequence of a fall is the so-called ‘long-lie’ (Lord et al., 2001) which is defined as remaining on the ground, following a fall, for longer than one hour (Wild et al., 1981,BMJ)
Approx 50% of those elderly who lie on the floor for an hour or more die within 6 months. (Wild et al.,1981,BMJ)
The solution to the problem of the long-lie:
Automatic detection of falls followed by transmission of an alert to the emergency services or care-giver
# # Falls, in the elderly, cause major problems for their welfare, confidence and happiness and greatly contribute to elderly health costs, morbidity and mortality. Based on statistics from the US (United States), we see that
# One in every three adults 65 years old or older, falls each year. Also 50% of nursing-home residents over the age 65 experience at least one fall every year. And (2/3) two thirds of elderly fallers experience a further falls within 6 months.
# Falls are the leading cause of injury deaths aswell as non-fatal injury among people 65 years and older. Based on statistics from the US.
# In 1998, roughly 9,600 people over the age of 65 died from fall-related injuries in the US. Of all fall deaths, more than 60% involve people who are 75 years or older. In Ireland the situation is just as serious.
# In 2002, 75% (3/4) of all fatal falls in Ireland occurred among people aged 65 and older. (Ireland 2002 vital statistics,

3. Current Autonomous long-term Fall-detection systems
Previously the end of a fall is characterized by an impact shock and/or the person being in a horizontal orientation
A number of long-term autonomous long-term fall-detection systems currently exist
Tunstall group fall detector
Doughty et al., 2000 Jnl. of Telemedicine and Telecare
(Tunstall group)
Noury et al., 2004, EMBC
Incorporated into a vest
Karantonis et al., 2006, IEEE Trans. Info. Technol. Biomed.
Worn at the waist
Fall sensor by Noury et al.
Fall sensor
# # The end of a fall may be characterized by an impact and by near horizontal orientation of the faller following the fall, most fall detectors detect one if not both of these characteristics.
# Tilt switches have been used to detect changes in posture and orientation historically these have been mercury tilt switches which do pose a major health hazard if this sensor breaks open, but accelerometers have become more popular for this task.
# Using successive transformations of the signals from the accelerometers Noury, obtained rate of change of inclination angle and posture angle, which was used to indicate a fall.
# Most primary fall-detection systems detect the shock received by the body upon impact to determine if a fall has occurred a typical sensor used for this is the accelerometer. They can be used to measure the retardation of the body when it is arrested by the ground, following a fall.
Karantonis et al. fall and activity monitor

4. The Fall-sensor A fall-sensor was developed which consists of:
μController
Bluetooth module
Tri-axial Accelerometer (MMA7261)
Battery
μSD card

5. The Vest
Developed using feedback from elderly subjects using questionnaire on donning, wearing the vest for an hour, then doffing the vest
The fall sensor can be located at the CHEST or LEFT UNDER-ARM, attached using Velcro
Zips closed at the front for ease of donning and doffing
100% polyester which is easy to clean, durable and stretchable
Additional elastic incorporated to support the sensor

6. Features of the Vest
The vest is made fully adjustable at the sides to fit subjects from 2XS to 3XL
Minimum amount of Fabric is located around the shoulders to allow for freedom of movement
Elastic was only present on one location where the sensor was to be located
Fall sensor

7. Objectives
Test the light-weight sensor to implement the fall detection algorithm
Gain user feedback on the design of the suitability of the vest to be worn by the elderly
Evaluate the threshold based algorithm to automatically distinguish between fall events and ADL.
# The objective of this study is to identify an optimum configuration of Accelerometer sensors from the trunk and thigh to automatically discriminate between falls and ADL.
This study describes the development of a threshold-based algorithm, capable of automatically discriminating between a fall-event and an ADL, using accelerometers.

8. Studies Two separate studies were completed:
A simulated fall-event and ADL study - used to establish the thresholds that would indicate that a fall had occurred. This was carried out using 10 young healthy subjects performing falls and normal activities
A long term Activities of Daily Living (ADL) study - to determine the accuracy of the algorithm in a long term trial. This was carried out using 10 elderly (>65 years) volunteers in a nursing home over the course of 4 weeks.
## for this research two separate studies were completed
# The first A simulated fall-event study - used to establish a number of different thresholds that would indicate that a fall had occurred
# The Second An Activities of Daily Living study - to determine the extent of miss-detection of ADL as a fall events.
# Accelerometer signals were acquired during simulated falls performed onto crash mats by healthy young subjects, # were compared with signals from Activities of Daily Living (ADL), performed by elderly people in their own homes.
It is postulated that recruiting elderly people to perform ADL testing of a fall-detection system increases the robustness of the test methodology.

9. Study 1 - Falls and ADL with Young healthy subjects
This was carried out using 10 young healthy subjects performing falls and normal ADL while wearing the vest and attached fall sensor
A total of 8 different fall types were performed
Falls in all four direction, performed with both legs straight and knee flexion
A total of 5 different ADL were performed which included
sitting, lying, standing and walking activities
# # This study involved 10 young healthy males performing simulated falls onto large foam crash mats under the supervision of a Physical Education professional. (Longitudinal, sagittal and medial-lateral accelerations were recorded from the trunk and thigh during each simulated fall-event.)
# The fall types were selected in order to realistically simulate the type of fall that may occur and cause injury to an elderly person.
Thus, each fall was performed with the subject initially in a standing position.
# A total of eight different fall types were completed with each fall-type being repeated three times, by each of the 10 subjects. Thus 240 falls were recorded in total.
# The crash mats used were gymnasium mats whose combined thickness was 0.76m, designed so that fall events could occur without injury.
# The subjects stood on the wooden support platform, designed to safely support the weight of a fully-grown person. (The dimensions of the platform were 1.22m (length) X 0.91m (width) X 0.76m (height).)
# The platform and crash mats were level, which provided a safe level environment for the subjects to fall upon.
The subjects were young (<30 years) healthy males. A total of 10 subjects were recruited for the study. The mean±standard deviation age, height and mass of the subjects were 23.7±2.2years, 1.78±0.058m and 75.9±5.1kg respectively. The exclusion criteria for this group was a history of any balance impairment, unexplainable spontaneous falls, neurological disease or uncorrected visual shortfall and all claimed to exercise regularly (>4 hours/week). All subjects, from this simulated fall-event study, gave written informed consent and the University of Limerick Research Ethics Committee approved the protocol.

10. Fall Detection Algorithm Study 1 results
The shaded algorithm was tested against the recorded data
Following this subject posture is continually monitored for
fall-alert: Lying for >75% of 10minutes
fall-recovery: Lying for < 75% of 10minutes
Sensitivity
Specificity
Accuracy
Chest
91.3%
99.6%
94.4%
LU arm
97.1%
98.6%
97.7%
detection of a suitable large impact, the subject’s orientation is then monitored to determine if a fall (fall-event) followed by a “long-lie” had occurred (fall-alert). A person was assumed to be in a lying position if the vertical accelerometer signal value was between 0.5g and -0.5g, this was considered to be a lying posture, suggested by Culhane et al. [11]. The fall detection algorithm which operates on the fall-detection sensor, is explained in Figure 3. By thresholding for an impact followed by a lying posture, it is envisioned that false-positive such as bumping into a object or falling into a sitting position can be eliminated.

11. Study 2 - Long-term ADL trial with Elderly subjects
A total of 10 elderly subjects wearing the vest and fall-sensor over the course of 4 weeks.
The 10 elderly were divided into 2 teams
Each team wearing the system for 2 week each for 8 hours a day from Monday to Sunday.
The trials took place in the nursing home “Benincasa” in the city of Ancona, Italy and were coordinated by COOSS Marche Onlus
Team 1
Team 2
Week 1
CAALYX Fall system (Chest)
rest
Week 2
Week 3
CAALYX Fall sensor (Left under-arm)
Week 4
CAALYX Fall system (Left under-arm)
## for this research two separate studies were completed
# The first A simulated fall-event study - used to establish a number of different thresholds that would indicate that a fall had occurred
# The Second An Activities of Daily Living study - to determine the extent of miss-detection of ADL as a fall events.
# Accelerometer signals were acquired during simulated falls performed onto crash mats by healthy young subjects, # were compared with signals from Activities of Daily Living (ADL), performed by elderly people in their own homes.
It is postulated that recruiting elderly people to perform ADL testing of a fall-detection system increases the robustness of the test methodology.

12. Method Sitting transitions Lying transitions Walking
Subjects donned the system in the morning and then proceeded to carry out their normal daily routine which included:
Sitting transitions
Lying transitions
Walking
Travelling by bus and car
Dining
Taking the stairs
Using the elevator
detection of a suitable large impact, the subject’s orientation is then monitored to determine if a fall (fall-event) followed by a “long-lie” had occurred (fall-alert). A person was assumed to be in a lying position if the vertical accelerometer signal value was between 0.5g and -0.5g, this was considered to be a lying posture, suggested by Culhane et al. [11]. The fall detection algorithm which operates on the fall-detection sensor, is explained in Figure 3. By thresholding for an impact followed by a lying posture, it is envisioned that false-positive such as bumping into a object or falling into a sitting position can be eliminated.

13. Method
Messages from the fall-sensor were relayed to the care-centre using Bluetooth to the Nokia N95 (which was attached to the subject in a pocket on the vest)
Messages were relayed further via the 3G network and the internet.
Messages included
Fall-event
Fall-alert
Fall-recovery
detection of a suitable large impact, the subject’s orientation is then monitored to determine if a fall (fall-event) followed by a “long-lie” had occurred (fall-alert). A person was assumed to be in a lying position if the vertical accelerometer signal value was between 0.5g and -0.5g, this was considered to be a lying posture, suggested by Culhane et al. [11]. The fall detection algorithm which operates on the fall-detection sensor, is explained in Figure 3. By thresholding for an impact followed by a lying posture, it is envisioned that false-positive such as bumping into a object or falling into a sitting position can be eliminated.

14. Fall-message transmission algorithm
Fall-messages associated with falls are sent by the fall-sensor to the mobile-phone and further propagated to the care-centre
Acknowledgement of receipt of the fall-messages are sent from the mobile phone to the fall-sensor and from the care-centre to the mobile-phone.
An acknowledgement of receipt of the fall-message from the mobile-phone to the care-centre is also sent back to the fall-sensor
No indication that this procedure is in progress is relayed to the elderly subject thus achieving automatic independent fall detection.
detection of a suitable large impact, the subject’s orientation is then monitored to determine if a fall (fall-event) followed by a “long-lie” had occurred (fall-alert). A person was assumed to be in a lying position if the vertical accelerometer signal value was between 0.5g and -0.5g, this was considered to be a lying posture, suggested by Culhane et al. [11]. The fall detection algorithm which operates on the fall-detection sensor, is explained in Figure 3. By thresholding for an impact followed by a lying posture, it is envisioned that false-positive such as bumping into a object or falling into a sitting position can be eliminated.

15. Method
The following messages thus appeared at the care-taker terminal:
Fall-event
Fall-alert
Fall-recovery
The relevant steps were then taken by the care-staff to ensure the elderly subjects safety.
These messages were also logged by the fall-sensor along with the raw accelerometer data to the μSD card
Care-taker site
detection of a suitable large impact, the subject’s orientation is then monitored to determine if a fall (fall-event) followed by a “long-lie” had occurred (fall-alert). A person was assumed to be in a lying position if the vertical accelerometer signal value was between 0.5g and -0.5g, this was considered to be a lying posture, suggested by Culhane et al. [11]. The fall detection algorithm which operates on the fall-detection sensor, is explained in Figure 3. By thresholding for an impact followed by a lying posture, it is envisioned that false-positive such as bumping into a object or falling into a sitting position can be eliminated.

16. Results – Fall Algorithm
In total 833 hours of monitoring was recorded over the course of 4 weeks onto the SD cards
Fall-events
115 true fall-events were recorded and transmitted by the fall-sensor
144 true fall-events were registered at the care-taker site
Fall-alerts
42 fall-alerts were recorded and transmitted by the fall-sensor
9 fall-alerts were registered at the care taker site
Fall-recoveries
73 fall-recoveries were recorded and transmitted by the fall-sensor
52 fall-recoveries were registered at the care taker site
The discrepancy between the fall-messages received and those registered is due
Bluetooth transmission errors with the short battery life of the N95 when Bluetooth is activated on the phone
The higher number of fall-events is due to the resending of these when no acknowledgements were received.

17. Results – Fall Algorithm
During the trials a number of falls did occur and were recorded onto the SD cards.
Here is one of those falls
A lady fell down while sitting and lay down for a short time

18. Results – Fall Algorithm
A lady fell down while sitting and lay down for a short time

19. Results – The vest
The vests were worn for the full length of the trial by all 10 elderly subjects
However feedback from the elderly subject and the nursing staff indicated that the vests were not appreciated.
The elderly subjects felt the vest was uncomfortable and they disliked wearing it for eight hours each day.
The nursing staff felt that they too bulky and too intrusive if to be worn under clothes, along with the fall-sensor.
# The resultant signals from accelerometer sensors on the trunk and thigh were also analysed to determine the extent of mis-detected ADL as fall-events by these signals. These signals produced better fall miss-detection percentages than any of their single axes counterparts.
# Thigh resultant accelerometer UFT, 12.9% of ADL mis-detected as fall-events.
# In an important result using the trunk resultant accelerometer UFT, 0% of ADL were mis-detected as fall-events, thus, no ADL were mistaken as fall-events-event from this sensors signal threshold, at the trunk.
# Thigh resultant accelerometer Upper threshold, 12.9% of ADL mis-detected as fall-events. The ADL “walking” and “getting in and out of a car seat” were responsible for 23 of the 31 (74.2%) mis-detected ADL as fall-events. Including the “lying on a bed activity”, these three activities were responsible for 26 of the 31 (83.3%) mis-detected ADL as fall-events.

20. Results – The vest A number of improvements were suggested:
The vest should be made larger but shorter so that the subjects have no difficulty in wearing and move about especially when using the toilet.
During the summer vest would not be appropriate for the high temperatures.
The vest should be made from more elastic material
# The resultant signals from accelerometer sensors on the trunk and thigh were also analysed to determine the extent of mis-detected ADL as fall-events by these signals. These signals produced better fall miss-detection percentages than any of their single axes counterparts.
# Thigh resultant accelerometer UFT, 12.9% of ADL mis-detected as fall-events.
# In an important result using the trunk resultant accelerometer UFT, 0% of ADL were mis-detected as fall-events, thus, no ADL were mistaken as fall-events-event from this sensors signal threshold, at the trunk.
# Thigh resultant accelerometer Upper threshold, 12.9% of ADL mis-detected as fall-events. The ADL “walking” and “getting in and out of a car seat” were responsible for 23 of the 31 (74.2%) mis-detected ADL as fall-events. Including the “lying on a bed activity”, these three activities were responsible for 26 of the 31 (83.3%) mis-detected ADL as fall-events.

21. Discussion\Conclusion
Through incorporating the fall sensor into a vest that can be worn by the elderly, it is considered that greater compliance with wearing and using a fall detection system can be achieved.
During the long term trials, 42 fall-alerts were recorded by the fall-sensor however only 9 were received at the care taker site
Thus indicating that further development of the fall-detection algorithm and the transmission protocol and method is required.
# # We have investigated single axis and resultant vector signals, from tri-accelerometers placed at the trunk and thigh, to determine if differences in their peak values could be used to discriminate between an ADL and fall-events.
# Using the trunk resultant tri-axial accelerometer signal threshold (UFT), 0% of ADL were mis-detected as fall-events. Thus, using this signal 100% of falls were correctly detected as fall-events and no ADL was mis-detected as fall-events, these percentages indicate that thresholding of this sensors signal is suitable, on it own, for incorporation into a primary fall-detection device.

22. Discussion\Conclusion
Also following feedback from the elderly subject’s it is clear that the vest were sufficient for a short term clinical trial.
However further development of the vest is required to make it more comfortable, breathable and easier to don and doff.
Further development of the system will include:
more accurate fall-detection and fall-message transmission algorithm,
more comfortable method of attachment, lighter and smaller sensor
as well as, mobility monitoring and energy expenditure measurement.
# # We have investigated single axis and resultant vector signals, from tri-accelerometers placed at the trunk and thigh, to determine if differences in their peak values could be used to discriminate between an ADL and fall-events.
# Using the trunk resultant tri-axial accelerometer signal threshold (UFT), 0% of ADL were mis-detected as fall-events. Thus, using this signal 100% of falls were correctly detected as fall-events and no ADL was mis-detected as fall-events, these percentages indicate that thresholding of this sensors signal is suitable, on it own, for incorporation into a primary fall-detection device.

23. Contact : alan.bourke@ul.ie
Questions ?
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Thank you.