We've provided a readme.pdf to explain how to use the dataset. Here, we reiterate some of that information to assist others in utilizing the dataset.
Please be aware that the files and the dataset are large (approx. 200GB.). It is advised to make sure there is ample storage space for downloading and unzipping. Please download one file at a time.
Dataset: Our data collection method involved cameras, subjects, environments, and guidelines for data simulation to elucidate the specifics of our process. Notably, our dataset, comprising 10,948 clips, stands out as the largest when compared to others that focus on falls recorded through egocentric cameras.
Equipment: Data was amassed using two wearable camera types: the OnReal G1 and CAMMHD Bodycams. The OnReal G1 is a compact mini action camera, with dimensions of 420 x 420 x 200 mm, and can capture videos in resolutions as high as 1080P at 30 fps. Conversely, the CAMMHD Bodycam, a larger camera measuring 800 x 500 x 300 mm, is outfitted with infrared sensors suitable for night vision. These cameras were strategically affixed to the human body at places like the waist and neck, allowing the collection of extensive visual, motion, and audio information across varied environments. The standard setting for data capture was the 1080p video mode at 30 frames per second. It's worth noting that the OnReal G1 frames consist of distinct R, G, B channels, whereas CAMMHD Bodycam frames feature three identical grayscale channels. This dataset, therefore, is a pivotal resource for this thesis, facilitating a thorough analysis of different events and activities.
Subject: For this study, we had 14 volunteer participants: 12 males and 2 females. This included 12 young, healthy individuals and 2 elderly subjects. All participants gave informed consent, understanding their data might be utilized for research and potentially be publicized. Most subjects (11 out of 14) finished the data collection encompassing four types of falls and nine types of non-falls, both indoor and outdoor. However, three participants couldn't complete the entire data collection due to personal reasons. This study yields significant insights into falls and non-fall behaviors, underscoring the dedication of the majority of our participants.
Environment: Our aim was a comprehensive study of both indoor and outdoor environments. We captured data across 14 different outdoor settings and 15 unique indoor spaces. To introduce variability, participants were prompted to change their positions or directions post each activity. Such an approach ensures a diversified dataset, letting us derive more reliable conclusions and insights.
Data Collection: Our data collection approach encompasses two main perspectives: visual and auditory. For visual data, we adhered to guidelines from existing literature; typical falls and related activities have a duration of 1-3 seconds. We proposed an exhaustive set of trials that cover 20 types of falls, each varying in direction and object interaction. Contrarily, specific guidelines for audio data are scarce, as past research largely centered on visual cues. Our audio dataset comprises three categories: subject audio, subject-object audio, and environment audio. To provide participants a realistic feel of falls, we showed them online videos of real-world fall incidents. These videos accurately render the auditory and visual elements of these events. Upon manual inspection of all clips, we discerned prevalent audio patterns. For falls, subject audio includes elements like yelling and moaning; subject-object audio encapsulates sounds of impacts, and environmental audio captures background noises like traffic or television. Importantly, not all clips contained every sound type. Non-fall activities were bifurcated into three groups based on their audio intensity. Our findings shed light on the audio patterns across activities, potentially enhancing subsequent research in this domain.
