For vehicles to operate in unmapped areas with some degree of autonomy, it would be useful to aggregate and store processed sensor data so that it can be used later. In this paper, a tool that records and optimizes the placement of costmap data on a persistent map is presented. The optimization takes several factors into account, including local vehicle odometry, GPS signals when available, local map consistency, deformation of map regions, and proprioceptive GPS offset error. Results illustrating the creation of maps from previously unseen regions (a 100 m × 880 m test track and a 1.2 km dirt trail) are presented, with and without GPS signals available during the creation of the maps. Finally, two examples of the use of these maps are given. First, a path is planned along roads that have been seen exactly once during the mapping phase. Secondly, the map is used for vehicle localization in the absence of GPS signals.

The article detailed the challenges in implementing a multisensor Android BLE application and a new methodology - SASC - was proposed to address these challenges. Benchmarking tests and free-living tests were conducted to validate the SASC implementation schemes. The benchmarking tests gave quantified results on metrics such as throughput, CPU loading on the smartphone and power consumption by the Android application for various number of sensors and varying connection intervals. The results of Android application throughput evaluation suggest that the maximum achievable throughput is ~20 kbps for a single sensor connection and ~70 kbps for a seven -sensor connection under SASC methodology. The results of the CPU loading and power consumption tests suggest that CPU loading and power consumption figures can be kept low by using a connection interval of 13.75 ms or greater. The proposed SASC methodology was also used in a real-life use case for gait data collection using SmartStep sensors, and a total of 19 days of human subject tests resulted in 100% data retrieval. These results suggest that the proposed methodology can serve as a foundation for applications that require managing multiple BLE connections in the world of IoT.

Automatic recognition of activities of daily living (ADL) is an important component in understanding of energy balance, quality of life, and other areas of health and well-being. In our previous work, we had proposed an insole-based activity monitor—SmartStep, designed to be socially acceptable and comfortable. The goals of the current study were: first, validation of SmartStep in recognition of a broad set of ADL; second, comparison of the SmartStep to a wrist sensor and testing these in combination; third, evaluation of SmartStep's accuracy in measuring wear noncompliance and a novel activity class (driving); fourth, performing the validation in free living against a well-studied criterion measure (ActivPAL, PAL Technologies); and fifth, quantitative evaluation of the perceived comfort of SmartStep. The activity classification models were developed from a laboratory study consisting of 13 different activities under controlled conditions. Leave-one-out cross validation showed 89% accuracy for the combined SmartStep and wrist sensor, 81% for the SmartStep alone, and 69% for the wrist sensor alone. When household activities were grouped together as one class, SmartStep performed equally well compared to the combination of SmartStep and wrist-worn sensor (90% versus 94%), whereas the accuracy of the wrist sensor increased marginally (73% from 69%). SmartStep achieved 92% accuracy in recognition of nonwear and 82% in recognition of driving. Participants then were studied for a day under free-living conditions. The overall agreement with ActivPAL was 82.5% (compared to 97% for the laboratory study). The SmartStep scored the best on the perceived comfort reported at the end of the study. These results suggest that insole-based activity sensors may present a compelling alternative or companion to commonly used wrist devices.

Footwear based wearable sensors are becoming prominent in many areas of monitoring health and wellness, such as gait and activity monitoring. In our previous research we introduced an insole based wearable system SmartStep, which is completely integrated in a socially acceptable package. From a manufacturing perspective, SmartStep's electronics had to be custom made for each shoe size, greatly complicating the manufacturing process. In this work we explore the possibility of making a universal electronics platform for SmartStep - SmartStep 3.0, which can be used in the most common insole sizes without modifications. A pilot human subject experiments were run to compare the accuracy between the one-size fits all (SmartStep 3.0) and custom size SmartStep 2.0. A total of ~10 hours of data was collected in the pilot study involving three participants performing different activities of daily living while wearing SmartStep 2.0 and SmartStep 3.0. Leave one out cross validation resulted in a 98.5% average accuracy from SmartStep 2.0, while SmartStep 3.0 resulted in 98.3% accuracy, suggesting that the SmartStep 3.0 can be as accurate as SmartStep 2.0, while fitting most common shoe sizes.

Footwear is an integral part of daily life. Embedding sensors and electronics in footwear for various different applications started more than two decades ago. This review article summarizes the developments in the field of footwear-based wearable sensors and systems. The electronics, sensing technologies, data transmission, and data processing methodologies of such wearable systems are all principally dependent on the target application. Hence, the article describes key application scenarios utilizing footwear-based systems with critical discussion on their merits. The reviewed application scenarios include gait monitoring, plantar pressure measurement, posture and activity classification, body weight and energy expenditure estimation, biofeedback, navigation, and fall risk applications. In addition, energy harvesting from the footwear is also considered for review. The article also attempts to shed light on some of the most recent developments in the field along with the future work required to advance the field.