Medidas preventivas de carácter particular para cada oficio.

The overall accuracy at detecting regular, semi-regular, or unstructured gaits is 84%. In order to determine where gait detection is challenging, a confusion matrix for gait detection was created, as shown in Table 4.1. It can be seen that regular gait was correctly identified the most often with an accuracy of 93%. The matrix indicates that when a participant was performing a regular gait activity, the classifier never misclassified the activity as unstructured gait, and misclassified regular gait as semi-regular gait 7% of the time. Semi-regular gait was correctly identified with 81% accuracy. When an error was made, semi-regular gait was misidentified as regular gait 2.2% of the time and as unstructured gait 17% of the time. This indicates that there is significant confusion between semi-regular and unstructured gaits. Unstructured gait was correctly identified 79% of the time. When misclassified, it was only misclassified as semi-regular gait, which occurred 21% of the time. This further reinforces the idea that it is more challenging to differentiate semi-regular and unstructured gaits than it is to identify regular gait.

In order to further understand challenges in detecting gaits, the accuracy of gait detection was also broken down according to sensor position, as shown in Table 4.2. From this table, it can be seen that the wrist and hip signals were classified correctly 88% and 89% of the time respectively, while the ankle signals was classified correctly only 77% of the time. Because the classifier was built without information about sensor location, this indicates that the features present in the ankle signal may differ from the features in the wrist ankle across gait types. With knowledge of sensor location, the algorithm for gait detection may be able to improve classification based on accounting

Wrist Hip Ankle

Accuracy 0.88 0.89 0.77

Table 4.2: Gait detection accuracy with respect to sensor location.

f (Hz) 0 0.5 1 1.5 2 2.5 3 3.5 4 |X[f]| 0 0.02 0.04 0.06 0.08 0.1

(a) Example with large peak magni- tude f (Hz) 0 0.5 1 1.5 2 2.5 3 3.5 4 |X[f]| 0 0.02 0.04 0.06 0.08 0.1

(b) Example with faster walking pace (above 2Hz) f (Hz) 0 0.5 1 1.5 2 2.5 3 3.5 4 |X[f]| 0 0.02 0.04 0.06 0.08 0.1

(c) Example with harmonics spaced out to .2 Hz and 3.5 Hz

Figure 4.7: Examples of variations within regular-gait, wrist worn FFTs. The variations shown do not cause misclassification.

for these differences.

Within regular gait, there are a number of variations within the resulting FFTs which were correctly classified, as shown in Figure 4.7. The sub-figures demonstrate FFTs resulting from a wrist- worn sensor in regular gait. Figure 4.7a demonstrates a dominant frequency with a peak magnitude of approximately 0.08. The dominant frequency occurs at 1.8 Hz, and a harmonic frequency can be seen at 2.7 Hz. Figure 4.7b demonstrates variation in the magnitude and frequency value of the dominant frequency. The magnitude is only 0.5 Hz in this figure, and the frequency is approximately 2.2 Hz. The resonant frequency is located at approximately 3.2 Hz. The participant with this FFT walked at a faster rate than the participant in 4.7a and exhibited a greater variation in walking rate, resulting in a lower maximum peak magnitude. Figure 4.7c demonstrates how the harmonics in the regular gait FFT can be spaced out differently by participant. This participant had a dominant frequency at 1.75 Hz with weaker harmonics at 0.2 Hz and 3.5 Hz.These variations in FFT did not cause misclassification of the data.

Examples of challenging variations in FFTs which did result in misclassification are demon- strated in Figure 4.8. Figure 4.8a shows an FFT resulting from a regular gait activity using a wrist-worn sensor. The participant in this example demonstrated significantly reduced arm swing

f (Hz) 0 0.5 1 1.5 2 2.5 3 3.5 4 |X[f]| 0 0.02 0.04 0.06 0.08 0.1

(a) Example of a regular gait activ- ity from a wrist-worn sensor which was misclassified as semi-regular gait. The participant demonstrated very little arm swing while walking.

f (Hz) 0 0.5 1 1.5 2 2.5 3 3.5 4 |X[f]| 0 0.02 0.04 0.06 0.08 0.1

(b) Example of a regular gait activ- ity from a hip-worn sensor which was misclassified as semi-regular gait.

f (Hz) 0 0.5 1 1.5 2 2.5 3 3.5 4 |X[f]| 0 0.02 0.04 0.06 0.08 0.1

(c) Example of a semi-regular gait ac- tivity seen from an ankle-worn sen-

sor. The FFT was misclassified as

regular gait. This participant was

the fastest to locate each hidden ob- ject and spent a greater proportion of their time with a steady stride.

Figure 4.8: Examples of variations across gait type and body position which resulted in misclassifi- cation of activities.

when walking, as compared to other participants. In Figure 4.8b, the FFT was generated from a wrist-worn accelerometer during a regular gait activity. While the characteristic dominant frequency peak is demonstrated, the number of harmonic peaks and the width of the dominant frequency peak caused a misclassification. These feature changes are likely the result of the participant having slight variations in their walking speed throughout the activity. Based on video review, these variations appear to have been the result of avoiding other pedestrians or uncertainty in the path to be fol- lowed. In Figure 4.8c, an ankle-worn sensor during semi-regular gait is incorrectly classified as a regular gait activity. This participant was the fastest to find each of the hidden objects, and so they spent a much larger proportion of their time walking with a steady stride up and down hallways.