COMPARACIONES ENTRE LOS TALLERES Y SUS CONTEXTOS

The fall velocity profiles derived by physics-based simulation are compared with genuine profiles. Specifically, 20 different fall events from YouTube videos are se- lected where actors faint after hyperventilation [147] as seen in Chapter 3. Those videos are the closest representations of fainting where actors fall rigidly uncon- scious to the ground and are a genuine source of falls. The videos were processed using [148] for calibration and vertical velocity (Vy) measurement. The process of

calibration fits a mesh to the ground plane and the user selects landmarks (i.e. ball, brick, fence, lamp post etc.) near the ground on at least three remote points on the image. A KLtracker is used to track the head and with the use of tracking points, the algorithm calculates its velocity.

An evaluation is conducted to measure the similarity of profiles of both the Open- Sim model and falling rod, where, Hausdorff distances are measured against Vy of

actual Youtube fall events. The usage and evaluation of the Hausdorff distance is discussed in 6.7.1 and 6.8.1. The average HD of the actual falls, when compared with an OpenSim model performing a forward fall, were 0.365m/s and 1.944m/s when compared with the falling rod. The reason why a forward fall was selected is discussed in 6.8.2. Also, the standard deviation was 0.078m/s when compared with the OpenSim model and 0.782m/s when compared with the rod model. These measurements show that a complex myoskeletal mode (OpenSim) provides more realistic simulated rigid falls more than the rod.

5.5

Conclusion

This Chapter has discussed a new methodology of using myoskeletal simulation for modelling falls and ADLs for the purpose of fall detection.

Different simulation models were evaluated, from a falling rod to a myoskeletal complex model. The human body is significantly different from the simple falling rod model, due to its articulation and muscular reflexes, whilst the rod model is a completely rigid object. A more complex model such as one derived by a myoskeletal simulator provides a more accurate representation of the human body.

Both rod and myoskeletal fall models were evaluated against genuine fall data from YouTube to prove their validity. Given these experiments, myoskeletal model simulation is feasible for the use of fall detection discussed in the next Chapter.

Fall detection based on

myoskeletal simulation

6.1

Introduction

This Chapter discusses the use of myoskeletal simulation as described in Chapter 5, applied for fall detection. With the use of simulation, the new algorithms described here try to overcome the issues of data scarcity of human fall data. The machine learning approaches used in Chapter 4 require a significant amount of data for training. The simulation tries to overcome the issue of data-driven approaches by modelling the fall events which are then used by a detection algorithm. Another issue of current algorithms is the lack of personalisation, that is, a classifier able to deal with different people falling irrespective of their physical characteristics or the type of fall.

Many studies on fall detection have been published in recent years, driven by the need for monitoring vulnerable independent livers and detecting accidents. Fur- ther impetus comes from the availability of cheap and easy-to-use depth cameras and other mobile sensors. Two broadly accepted approaches for detecting falls are summarised in recent review studies [72, 149]: i) ad-hoc methods based on empirical observations and ii) pattern recognition methods that are trained us- ing machine learning (ML). Both approaches require pre-recorded training data of falls that are normally staged and performed by volunteers or actors to tune their performances for fall detection. Nevertheless, human subjects may hesitate

to perform a fall and also the acting of a fall might be directed in such a way that is not realistic, or similar to an actual fall event, e.g. fainting [28]. However, the quantity and availability of fall event data is low compared to other tasks of action/event recognition.

A common approach to detect fall events is usually performed by a single model which ignores physical body characteristics (such as a person’s height) and as a result the dynamics of the fall may differ accordingly. Existing fall datasets (as discussed in Chapter 3) are based on a small number of human subjects with lim- ited body variability in sex, age, height and weight distribution. In a trainable algorithm, the requirement is to have a dataset that is large enough to capture natural variations of individual characteristics, not only to cover the data require- ments of the machine learning algorithm but also to properly cover a range of people’s physical characteristics and fall types. All data samples of fall and ADL events are tested via the same procedure that has been trained using a small set of data from human subjects of limited body variability (e.g. height). Hence, al- gorithms trained on limited datasets have questionable performance when applied to the wider population. A physics-based myoskeletal simulation (as discussed in Chapter 5) provides the opportunity for customising the activity based on the body characteristics and the environment in which the fall occurs.

One solution to address the lack of data is to use an approach that is customised to a person’s physical characteristics. [150] use accelerometers to make a personalised fall detector recording the acceleration patterns of ADLs during a calibration. An anomaly detection algorithm is then used to identify falls. However, this approach determines its detection decisions based on human subjects with small differences in their physical characteristics (e.g. an 8cm height variation) raising doubts about performance if the differences were larger.

Three novel approaches are discussed in this Chapter which promote the use of simulation to address the issue of the scarcity and quality of training data to im- prove fall detection algorithms by customising fall events using myoskeletal simu- lations and for the purpose of personalisation. One of the proposed methodologies extracts a person’s height and pre-fall body orientation from depth cameras to simulate customisable falls (with height and orientation as parameters). The de- rived data from the simulations are then used as training examples or models of falling behaviour. The second approach is capable of using data from simulating

falls and ADLs customised by the model’s height and performs the detection pro- cedure without a machine learning technique. The evaluation will use depth and YouTube data. A third approach uses a different feature to apply the myoskeletal simulation fall detection approach on accelerometer data from a wearable device.

Since the only source of video recordings of falls are based on acted falls, this data are used to evaluate the performance of the detector based on simulation. Experiments are presented based on three methodologies: (i) a hybrid approach, which describes a simple methodology using simulation data and acted data (ii) a fully simulation-based on velocity measurements method and (iii) an impact based fall detection using myoskeletal simulation.