Non-vision based methods for fall detection

There are many non-vision based methods for fall detection in the global market today. For these methods, different sensors are used to capture the sound, vibration and human body movement information and such information is applied to determine when a fall happens [22].

The most popular used sensors in this non-vision category are accelerom- eter and gyroscopic sensors. For accelerometer sensors, they are typi- cally based on small integrated circuits consisting of two surface micro machined capacitive sensing devices and a signal conditioning unit con- tained in a single integrated circuit package. This type of sensor is typically used to measure acceleration, tilt angle θ, the direction of the acceleration of a body along the X, Y and Z axes due to movement and acceleration due to gravity [23]. These sensors generate a signal that acts as input data to a computer system or an embedded system which

then analyses the data to detect falls. Figure 2.2 shows at a high level the sensor and the principle of using accelerometers for fall detection.

Accelerom eters Sensor (IC s) θ θ

Figure 2.2. Block diagram of basic principle of accelerometer sensor and fall detection system [23].

Veltink et al. [24] were the first to utilise a single axis acceleration sen- sor to distinguish dynamic and static activities in 1996.

Acceleration sensors were placed over the chest and at the feet to ob- serve the changes. Since then, several studies of using accelerometer and gyroscope sensors have been published in the last 10 years [25]. The simple and basic approach is to use the sensors (accelerometer and or gyroscope) based on a threshold value as a fall detection process denoting a fall when the acceleration is maximum.

Acceleration thresholds for fall detection have been studied using triax- ial accelerometric measurements at the waist, wrist, and head for dif- ferent fall events (forward, backward and lateral). A study has shown that the measurements from the waist and head have potential to distin- guish between falls and activities of daily living [26]. Besides, triaxial

accelerometers worn on the chest were used in [27] and [28]. They de- tected certain falls with 98.9% accuracy by applying a simple threshold to the acceleration.

Kangas et al. [26] designed a single three axis acceleration sensor to attach to the subject’s body in different positions: head, waist and wrist to sense fall accidents. The dynamic and static acceleration com- ponents measured from these acceleration sensors were compared with proper thresholds to determine a fall. The results showed that a simple threshold based algorithm was appropriate for certain falls, and opti- mum sensing effect could be achieved at the head and waist.

The other sensor which can be used for fall detection is a gyroscope Figure 2.3, which measures orientation and consists of a spinning wheel whose axis is free to take any orientation.

Figure 2.3. Block diagram of basic principle of digital gyroscope sen- sor within fall detection system [29].

ping an object with the gyroscope to measure the orientation along three axes, it is possible to determine the orientation of the object and the changes in orientation, from which the angular velocity can then be computed [29].

Bourke and Lyons [30] used a biaxial gyroscope worn on the chest to measure angular velocity data based on thresholds and optical mo- tion capture to distinguish between falls and non-fall activities. They showed that they could detect falls with 100% accuracy.

Sometimes, accelerometers and gyroscope can be used in a combined way in order to obtain a better result.

Tong et al. [31] used a combination of accelerometers and a gyroscope to detect acceleration and orientation of the subject for detecting falls. The data from sensors were processed locally and a call sent wirelessly to the main server to flag a fall. The system was attached to the chest or back of the person, which had been determined as the best option after considerable research on positioning of kinematic sensors. In the case of a fall, the accelerometer sensed acceleration greater than a set threshold value and the gyroscope determined the orientation of the subject; if there was a fall, an emergency call was sent to the care peo- ple. Moreover, using both an accelerometer and two gyroscopes could be used to detect the forward falls. The study showed that one could be able to detect successfully all 60 falls and differentiate between falls and activities of daily living with no false positives.

Nyan et al. [32] used a three-dimensional accelerometer and 2D gyro- scope based on a body area network (BAN). The experimental results showed that falls could be detected with an average time of 700ms be- fore the impact fall detection occurred, with no false alarms, and a fall

detection rate of 95.2% can be achieved.

In addition, other researchers have used acoustic and vibration sensors to detect falls. Li et al. [33] developed an acoustic fall detection system, which automatically detected a fall and reported it to the care giver. The study used an 8-microphone circular array which provided a better three-dimensional estimation of the sound location. Although promis- ing results were obtained in their experiment, whether this algorithm was workable on more realistic datasets, such as falls in presence of noises, needed more testing.

In Mihail et al. [34], an acoustic FAll DEtection system (FADE) that will automatically signal a fall to the monitoring care giver was designed. A linear array of an electric microphone [35] condenser acoustic sensors was applied to obtain the audio signal; mel frequency cepstral coeffi- cients (MFCC) features were extracted and the k-th nearest neighbor method was applied to determine a fall and non-fall activity. The sound was considered a false alarm if it came from a source located at a height higher than two feet so that the false alarm rate could be reduced. Their method seemed to be successful on a limited dataset, more experiments were needed however to determine whether their method would be suc- cessful in real scenarios.

Alwan et al. [36] proposed a design for a floor vibration-based fall de- tection system that was completely passive and unobtrusive to the res- ident.

The system used a special piezoelectric sensor coupled to the floor sur- face by means of mass and spring arrangement. Successful differentia- tion between the vibration patterns of a human fall from other activities of daily living and from the falls of other objects was achieved. Lab-

oratory tests were conducted using anthropomorphic dummies. The results showed 100% fall detection rate with minimum potential for false alarms. The drawback of this approach was the limited range of the vibration sensor; i.e. only six meters. Moreover, the vibrations couldn’t be detected on all kinds of floor materials. The piezoelectric sensor only captured the signal produced by the floor vibration; it was robust to the background noises.

Although non-vision based methods show a potentially wide application in the fall detection field; however, several problems exist. They are intrusive (accelerometer and gyroscope) and easily affected by noises in the home environment (acoustic and vibration based methods). In order to overcome these problems, intelligent vision based fall detection techniques are next considered.