Since january 1st of this year, people over 65 years old had a fall in Europe, sometimes with serious consequences.
Since january 1st of this year, people over 65 years old had a fall in Europe, sometimes with serious consequences.
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Various practical applications have been developed in the field of detection, with devices using these systems in combination or alone in order to offer alarm devices that send a signal when a fall occurs.
Devices worn on the belt or around the neck
They use direct detection systems.Seniors often forget to wear them or can be unable to press the button when a serious fall occurs.
On the contrary, they can also be triggered by accident in the case of an involuntary impact.
- Devices worn around the wrist
They use direct detection systems. They have the advantage of being less noticeable but present the same disadvantages as the systems worn on the belt or around the neck.
- Multifunction devices
This type of device is worn around the wrist like a big watch. It is equipped with sensors that constantly check various vital functions such as heart rhythm or blood pressure. It is combined with a fall detection system which must however be triggered manually.
Hip protectors serve as a protective shield around the neck of the thighbone, which is the most fragile part of the hip bones. They absorb shocks to avoid any injury following an impact on the hip during a fall.
They most often consist in underwear with side pockets in order to insert semirigid protective equipment.
According to a first study carried out in Finland in 2000 with over 1,800 participants, hip protectors appear to be able to greatly reduce the number of hip fractures among frail seniors.
However, other studies, carried out more recently and with more reliable methodologies, had less optimistic results, notably among frail patient living in nursing homes; in this case, protectors do not seem to be very effective.
- The verticality alarm
This direct detection system goes off when senior patients go from a sitting to a standing position, even when no fall has occurred. There is therefore no systematic connection between the alarm being triggered and falls.
This device is mostly used for people in wheelchairs or spending much time seated because it tends to drive those people to spend even more time sitting down, leading to a loss of gait reflexes and a higher fall risk.
- Video surveillance
It uses indirect detection systems and is highly expensive to install.Like any video surveillance system, it is highly intrusive.
- Mobile or cordless telephones
They can be used as a protective device, but they have the same disadvantages: they must be triggered manually and you must remember to keep them on you at all times or you might not be able to access them when you need to.
The Physical Medicine and Rehabilitation unit at the hospital in Poitiers has created a mobile unit for the prevention of falls among seniors. It includes physicians (re-educators and geriatricians) and physiotherapists. Its purpose is to improve knowledge in the specific field of falls among the elderly, and to make it possible for people with a disability to live better with their condition (prevention, re-education, rehabilitation) and continue living as autonomously as possible in their usual environment.
At first, the unit will intervene in nursing homes.
Duty shifts will then be gradually established, followed by in-home interventions. A network will be formed between city physicians, physiotherapists, nurses and social workers from the region working with the mobile unit.
Screening for postural and balance impairments among seniors constitutes a major issue in terms of public health. A simple therapeutic strategy could be organised to remedy static and dynamic impairments.
The consequences of a fall can lead to a risk of death for senior patients; the action of this body will aim directly at reducing this major risk they are running.
The objective is to analyse senior's posture and gait initiation in a situation that is as similar as possible to their usual living environment, and thereby be able to determine the predictive parameters for falls among elderly persons.
This assessment will be performed at home and include taking the patient's medical history and a complete medical check-up. The team will be using a mobile posturometer that can be used in patients' homes in order to assess their posture in their usual living environment. This device makes it possible to quickly analyse balance and posture with eyes open, then closed.This assessment performed in the person's living environment constitutes an original and pragmatic tool. This evaluation strategy appears to be able to provide significant information for the assessment of seniors' fall risk. In particular, it makes it possible to revise the rehabilitation or compensatory strategies.
Many highly promising projects are currently under way. We will keep this section up-to-date with notable scientific developments.
THE VARIOUS FALL DETECTION MODES:LINES OF RESEARCH*
(*source: N. Noury, TIMC-IMAG Laboratory, University of Grenoble I)
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Detecting changes in the rate of vertical motion
This detection system is based on identifying fast acceleration in the rate of vertical motion, something that occurs during a fall
As it rises linearly over time, the vertical speed that corresponds to the constant acceleration due to the gravitational pull will exceed the speed threshold of controlled motion during a fall.
The Wu team at the University of Vermont in the USA has shown, by analyzing video footage of patients falling, that the vertical and horizontal speeds were dissimilar during controlled motion but became practically similar during a fall. Hence the modifications to these speeds is a good way to characterize a fall. Nait-Charif and Rougier too studied falling by analyzing video footage, but in this case through head motion and using the vertical and longitudinal speed thresholds.
The main problem with these detection methods is that the detection thresholds are hard to determine due to the significant variability in the controlled motion speed of individuals (the variability between individuals). If these thresholds are too low, they lead to the detection of falls that are not falls at all (false positives). On the other hand, if these thresholds are too high, then there is the risk of missing undetected falls (false negatives). A number of teams are attempting to overcome these error causes (Noury (University of Grenoble) through supervised learning or Depeursinge (CSEM, Lausanne) through unsupervised learning).
A number of different kinds of sensors have been put forward, especially accelerometers and gyroscopes (rotation), with a localization at the upper body level (Hwang, Noury), in line with the sacrum (Prado, Spain), at the waist (Matie) or even using the head.
To go beyond just simple detection, a Japanese team (Fuyaka, Japan) recently proposed that critical phase detection could trigger the deployment of an airbag!
Detecting the impact with the ground
On impact, the motion speed is suddenly reversed. This detection system is based on identifying the reversal in the direction of acceleration.
The sensors used are accelerometers and impact detectors, with a localization at the waist level (Williams and Doughty, Ireland), at the center of gravity (Lindeman, Germany) or at the upper body level (Bourke, Ireland).
The main difficulties arise when it comes to determining the direction of the trajectory along with the "signature" of the impact, the cause of false positives.
Laying down on the floor
These detection systems seek to identify when a body is horizontal, meaning that it has fallen.
There are a number of techniques available: Some sensors distributed over the body detect a horizontal position ("dead body"), or a loss of contact between feet and the ground (Tamura, Japan). However, when the person lies down on a bed or on a sofa, the detectors may take that as a fall (false positive).
Another method used is sensors in the floor that detect the body's contact with the floor ("actimetric floor").
It should be noted that some laboratories are interested in the noise of impact at the time of the fall: they are developing noise sensors that can isolate the impact noise and raise the alert. Naturally these techniques must be perfectly specific so as to avoid raising an alarm every time a book is dropped or a pair of glasses!
Prolonged immobilization
Detection is based on a lack of motion
The sensors that detect the immobility that is supposed to follow a fall may use various methods: accelerometer vibration meters (when all motion stops), infrared presence sensors or even video cameras.
In this way, the motion of different body segments has been analyzed using a mobile phone (Tamura, Japan). The absence of any motion has also been analyzed by a ceiling mounted video camera (Mihailidis, University of Toronto) or using infrared presence sensors (Noury, University of Grenoble).
If the immobilization time between two motions that are considered to be critical by the device, prior to raising the alarm is too short, then false positives are obtained. If on the other hand, this idle time is too long, then the reaction time will be too long.
Video raises a number of technical problems in terms of control over the field of vision, ambient light or vision in space. This method also gives rise to ethical problems due to the intrusion that it represents.
One proposal is to integrate decision making algorithms directly at the camera level so as to avoid transferring the picture outside of the home.
Scenario analysis
Detection is based on a combination of parameters that are characteristic of a critical situation
Various sensors record data linked to the environment conditions prevalent at the time of the fall and what happens immediately afterwards, information that is then evaluated by algorithmic analysis.
Measures have been developed from cross referencing actimetric data recorded by infrared sensors, microphones or even video cameras equipping the home.
Other mechanisms that are especially promising are based on an algorithmic analysis of cross referenced data from accelerometers that detect falling and infrared detectors that detect prolonged immobilization (Noury, University of Grenoble).
