Distracted pedestrians, like distracted drivers, are an increasingly dangerous threat and precursors to pedestrian accidents in urban communities, often resulting in grave injuries and fatalities. Mitigating such hazards to pedestrian safety requires employment of pedestrian safety systems and applications that are effective in detecting them. Designing such frameworks is possible with the availability of sophisticated mobile and wearable devices equipped with high-precision on-board sensors capable of capturing fine-grained user movements and context, especially distracted activities. However, the key technical challenge is accurate recognition of distractions with minimal resources in real-time given the computation and communication limitations of these devices. Several recently published works improve distracted pedestrian safety by leveraging on complex activity recognition frameworks using mobile and wearable sensors to detect pedestrian distractions. Their primary focus, however, was to achieve high detection accuracy, and therefore most designs are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system, we design an efficient and real-time pedestrian distraction detection technique that overcomes some of these shortcomings. We demonstrate its practicality by implementing prototypes on commercially-available mobile and wearable devices and evaluating them using data collected from participants in realistic pedestrian experiments. Using these evaluations, we show that our technique achieves a favorable balance between computational efficiency, detection accuracy and energy consumption compared to some other techniques in the literature.
It is well recognized that walking while using mobile phones will make people more susceptible at various risks. Existing studies to improve smartphone users' safety are mainly limited to detecting incoming vehicles. They are not able to address some more common and equally dangerous accidents such as trips, falling from stairs, platforms or falling into an open manhole. These hazards are generally caused by sudden change of ground. In this paper, we propose UltraSee, the first system that is able to detect sudden change of ground for pedestrian mobile phone users. UltraSee augments smartphones with a small ultrasonic sensor which can detect the abrupt change of distance ahead. UltraSee also leverages the context information of smartphone usage such as screen status and holding orientation to improve detection accuracy and reduce energy consumption as well as unnecessary alarms. We have carried out extensive experiments in different scenarios and by different users. The results show that UltraSee can achieve accident detection rate of 94 with false positive rate of 4.4 and reduce unnecessary alarms by 90 . In terms of energy consumption, UltraSee costs only about 20 energy compared to the existing works that only rely on smartphone cameras. Recently, multitasking on smart phones during navigation has emerged as a problematic social behavior due to its potential danger. In this paper, we propose to alleviate the situation by employing a sensor system for obstacle detection and aid the multi-tasking user so that one can safely navigate and carry out the on-going secondary task as effectively as possible. As such, we have implemented an ultrasonic sensor system interfaced into the smart phone that can constantly appraise the user of the incoming obstacles. We ran experiments to validate our approach, checking whether such a system would help user bump less into obstacles than without, and observe their multitasking behavior such as the physical attentional switch. Our experiments have shown that with the aid of the sensor system, the user's attention switch was significantly reduced, however, there was no differences in the performance (e.g. no. of collision) because the momentary strategic spatial and path planning was quite effective with slow pace and light pedestrian traffic. We conclude therefore human dual or multitasking ability is sufficient to overlap "casual" video watching and "slow" navigation. However, with more stringent task, e.g. text messaging and heavier traffic, we expect the reduced attention shift to ultimately improve the performance as well. This video is a demonstration of the work discussed in our full paper available in the MobiSys'15 proceedings. The video illustrates a sensing technology for fine-grained location classification in an urban environment, for enhancing pedestrian safety. Our system seeks to detect the transitions from sidewalk locations to in-street locations, to enable applications such as alerting texting pedestrians when they step into the street. Existing positioning technologies are not sufficiently precise to allow distinguishing a position on the sidewalk from a position in the street, as explored in our previous work. To this end, we use shoe-mounted inertial sensors for location classification based on surface gradient profile and step patterns. This approach is different from existing shoe sensing solutions that focus on dead reckoning and inertial navigation. The shoe sensors relay inertial sensor measurements to a smartphone, which extracts the step pattern and the inclination of the ground a pedestrian is walking on. This allows detecting transitions such as stepping over a curb or walking down sidewalk ramps that lead into the street. We carried out walking trials in metropolitan environments in United States (Manhattan) and Europe (Turin). The results from these experiments show that we can accurately determine transitions between sidewalk and street locations to identify pedestrian risk. Mobile device use while walking, or eyes-busy mobile interaction, is a leading cause of life-threatening pedestrian collisions. We introduce CrashAlert, a system that augments mobile devices with a depth camera, to provide distance and location visual cues of obstacles on the user's path. In a realistic environment outside the lab, CrashAlert users improve their handling of potential collisions, dodging and slowing down for simple ones while lifting their head in more complex situations. Qualitative results outline the value of extending users' peripheral alertness in eyes-busy mobile interaction through non-intrusive depth cues, as used in CrashAlert. We present the design features of our system and lessons learned from our evaluation.