We address the challenge of multi-LiDAR interference, an issue of growing importance as LiDAR sensors are embedded in a growing set of pervasive devices. We introduce a novel approach named D2SR, enabling decentralized interference detection, mitigation, and recovery without explicit coordination among nearby LiDAR devices. D2SR comprises three stages: (a) Detection, which identifies interfered frames, (b) Mitigation, which performs time-shifting of a LiDAR’s active period to reduce interference, and (c) Recovery, which corrects or reconstructs the depth values in interfered regions of a depth frame. Key contributions include a lightweight interference detection algorithm achieving an F1-score of 92%, a simple yet effective decentralized de-synchronization mechanism, and a lightweight depth recovery pipeline that preserves high throughput processing on edge devices. Evaluation on Nvidia Jetson devices demonstrates D2SR’s efficacy: under static settings, D2SR accurately detects interference in 93% of cases (recall=82%) and reduces the depth estimation error by 27% (RMSE= 38.7 cm, compared to RMSE= 60.6 cm for a baseline without D2SR). Furthermore, D2SR is able to reduce the fraction of interfered frames by 75.1% and reduce the depth estimation error (for interfered frames) by 24.9% even for a moving robot scenario.

We present LiLoc , a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. LiLoc stands out with two key differentiators. First, unlike traditional localization approaches, our method remains robust in dynamically changing environments, adeptly handling varying crowd levels and object layout changes. Second, LiLoc is independent of pre-built static maps, employing dynamically updated point clouds from infrastructural-mounted LiDARs and LiDARs on individual IoT devices. For fine-grained, near real-time tracking, LiLoc intermittently utilizes complex 3D “global” registration between point clouds for robust spot location estimates. It further complements this with simpler “local” registrations, continuously updating IoT device trajectories. We demonstrate that LiLoc can (a) support accurate location tracking with location and pose estimation error being ≦7.4 cm and ≦3.2°, respectively, for 84% of the time and the median error increasing only marginally (8%), for correctly estimated trajectories, when the ambient environment is dynamic; (b) achieve a 36% reduction in median location estimation error compared to an approach that uses only quasi-static global point cloud; and (c) obtain spot location estimates with a latency of only 973 msec. We also demonstrate how LiLoc efficiently integrates low-power inertial sensing, using a novel integration of inertial-based displacement to accelerate the local registration process, to enhance localization energy efficiency and latency.

This work demonstrates the VGGlass system, which simultaneously interprets human instructions for a target acquisition task and determines the precise 3D positions of both user and the target object. This is achieved by utilizing LiDARs mounted in the infrastructure and a smart glass device worn by the user. Key to our system is the union of LiDAR-based localization termed LiLOC and a multi-modal visual grounding approach termed RealG(2)In-Lite. To demonstrate the system, we use Intel RealSense L515 cameras and a Microsoft HoloLens 2, as the user devices. VGGlass is able to: a) track the user in real-time in a global coordinate system, and b) locate target objects referred by natural language and pointing gestures.

Fine-grained, unobtrusive monitoring of gym exercises can help users track their own exercise routines and also provide corrective feedback. We propose W8-Scope, a system that uses a simple magnetic-cum-accelerometer sensor, mounted on the weight stack of gym exercise machines, to infer various attributes of gym exercise behavior. More specifically, using multiple machine learning models, W8-Scope helps identify who is exercising, what exercise she is doing, how much weight she is lifting, and whether she is committing any common mistakes. Real world studies, conducted with 50 subjects performing 14 different exercises over 103 distinct sessions in two gyms, show that W8-Scope can achieve high accuracy-e.g., identify the weight used with an accuracy of 97.5%, detect commonplace mistakes with 96.7% accuracy and identify the user with 98.7% accuracy. Moreover, by adopting incremental learning techniques, W8- Scope can also accurately track these various facets of exercise over longitudinal periods, in spite of the inherent natural changes in a user's exercising behavior.

We present ERICA, a digital personal trainer for users performing free weights exercises, with two key differentiators: (a) First, unlike prior approaches that either require multiple on-body wearables or specialized infrastructural sensing, ERICA uses a single in-ear "earable" device (piggybacking on a form factor routinely used by millions of gym-goers) and a simple inertial sensor mounted on each weight equipment; (b) Second, unlike prior work that focuses primarily on quantifying a workout, ERICA additionally identifies a variety of fine-grained exercising mistakes and delivers real-time, in-situ corrective instructions. To achieve this, we (a) design a robust approach for user-equipment association that can handle multiple (even 15) concurrently exercising users; (b) develop a suite of statistical models to detect several commonplace repetition-level mistakes; and (c) experimentally study the efficacy of multiple in-situ corrective feedback strategies. Via an end-to-end evaluation of ERICA with 33 participants naturally performing 3 dumbbell-based exercises, we show that (a) ERICA identifies over 94% of mistakes during the first 5 repetitions of a set, (b) the resulting feedback is viewed favorably by 78% of users, and (c) the feedback is effective, reducing mistakes by 10+% during subsequent repetitions.

In this paper, we present I4S, a system that identifies item interactions of customers in a retail store through sensor data fusion from smartwatches, smartphones and distributed BLE beacons. To identify these interactions, I4S builds a gesture-triggered pipeline that (a) detects the occurrence of "item picks", and (b) performs fine-grained localization of such pickup gestures. By analyzing data collected from 31 shoppers visiting a midsized stationary store, we show that we can identify person-independent picking gestures with a precision of over 88%, and identify the rack from where the pick occurred with 91%+ precision (for popular racks).

In this paper, we present LiveLabs, a first-of-its-kind testbed that is deployed across a university campus, convention centre, and resort island and collects real-time attributes such as location, group context etc., from hundreds of opt-in participants. These venues, data, and participants are then made available for running rich human-centric behavioural experiments that could test new mobile sensing infrastructure, applications, analytics, or more social-science type hypotheses that influence and then observe actual user behaviour. We share case studies of how researchers from around the world have and are using LiveLabs, and our experiences and lessons learned from building, maintaining, and expanding Live-Labs over the last three years.

We investigate the possibility of using a combination of a smartphone and a smartwatch, carried by a shopper, to get insights into the shopper's behavior inside a retail store. The proposed IRIS framework uses standard locomotive and gestural micro-activities as building blocks to define novel composite features that help classify different facets of a shopper's interaction/experience with individual items, as well as attributes of the overall shopping episode or the store. Besides defining such novel features, IRIS builds a novel segmentation algorithm, which partitions the duration of an entire shopping episode into atomic item-level interactions, by using a combination of feature-based landmarking, change point detection and variable-order HMM-based sequence prediction. Experiments with 50 real-life grocery shopping episodes, collected from 25 shoppers, we show that IRIS can demarcate item-level interactions with an accuracy of approx. 91%, and subsequently characterize item-and-episode level shopper behavior with accuracies of over 90%.

Driven by the rapid market growth of sensors and beacons that offer Bluetooth Low Energy (BLE) based connectivity, this paper empirically investigates the performance characteristics of the BLE interface on multiple Android smartphones, and the consequent impact on a proposed BLE-based service: continuous indoor location. We first use extensive measurement studies with multiple Android devices to establish that the BLE interface on current smartphones is not as "low-energy" as nominally expected, and establish that continuous use of such a BLE interface is not feasible unless we choose a moderately large scan interval and a low duty cycle. We then explore the implications of such constraints, on the parameters of a smart phone's BLE stack, on the accuracy of a BLE-based indoor localization techniques. We show that while RF-based indoor location can be highly accurate (80% of estimates have errors less than or equal to 4 meters) for stationary users only if the density of beacons is high, the combination of (large scan interval, low duty cycle) causes the location error to degrade significantly for moving users. These results provide practical insights into the use cases and limitations for future BLE-based mobile services.

Mobile games, and especially multiplayer games are a very popular daily distraction for many users. We hypothesise that commuters travelling on public buses or trains would enjoy being able to play multiplayer games with their fellow commuters to alleviate the commute burden and boredom. We present quantitative data to show that the typical one-way commute time is fairly long (at least 25 minutes on average) as well as survey results indicating that commuters are willing to play multiplayer games with other random commuters. In this paper, we present GameOn, a system that allows commuters to participate in multiplayer games with each other using p2p networking techniques that reduces the need to use high latency and possibly expensive cellular data connections. We show how GameOn uses a cloud-based matchmaking server to eliminate the overheads of discovery as well as show why GameOn uses Wi-Fi Direct over Bluetooth as the p2p networking medium. We describe the various system components of GameOn and their implementation. Finally, we present numerous results collected by using GameOn, with three real games, on many different public trains and buses with up to four human players in each game play.