Xinlong Jiang

TOSELM: Timeliness Online Sequential Extreme Learning Machine

For handling data and training model, existing machine learning methods do not take timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods.

Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization

Abstract Wi-Fi-based indoor localization with high capability and feasibility needs to implement lifelong online learning mechanism. However, the characteristic of Wi-Fi is wide variability, which lies in not only the fluctuation of signal strength value, but also the increase or decrease in the number of access points (APs). The traditional algorithms are effective for signal fluctuation, but cannot handle the dimension-changing problem of features caused by increase and decrease in APs’ number. To solve this problem, we propose a Feature Adaptive Online Sequential Extreme Learning Machine (FA-OSELM) algorithm. It can transfer the original model to a new one with a small number of data with new features, so as to make the new model suitable for the new feature dimension. The experiments show that the FA-OSELM can get higher accuracy with a small amount of new data, and it is an effective method to make lifelong indoor localization practical.

Semi-supervised deep extreme learning machine for Wi-Fi based localization

Along with the proliferation of mobile devices and wireless signal coverage, indoor localization based on Wi-Fi gets great popularity. Fingerprint based method is the mainstream approach for Wi-Fi indoor localization, for it can achieve high localization performance as long as labeled data are sufficient. However, the number of labeled data is always limited due to the high cost of data acquisition. Nowadays, crowd sourcing becomes an effective approach to gather large number of data; meanwhile, most of them are unlabeled. Therefore, it is worth studying the use of unlabeled data to improve localization performance. To achieve this goal, a novel algorithm Semi-supervised Deep Extreme Learning Machine (SDELM) is proposed, which takes the advantages of semi-supervised learning, Deep Leaning (DL), and Extreme Learning Machine (ELM), so that the localization performance can be improved both in the feature extraction procedure and in the classifier. The experimental results in real indoor environments show that the proposed SDELM not only outperforms other compared methods but also reduces the calibration effort with the help of unlabeled data.

Constraint Online Sequential Extreme Learning Machine for lifelong indoor localization system

As an important technology in LBS (Location Based Services) field, Wi-Fi based indoor localization suffers signal fluctuation problem which prevents lifelong and high performance running. With the fluctuation of wireless signal over time, fingerprints collected at the same location become different; therefore existing model cannot fit the new collected data well, which decreases the localization accuracy. In this paper, a novel indoor localization method COSELM (Constraint Online Sequential Extreme Learning Machine) is proposed, utilizing incremental data to update the old model and overcome the fluctuation problem. The performance of COSELM is validated in real Wi-Fi indoor environment. Compared with OSELM, it can improve more than 5% localization accuracy on average; and in contrast to batch learning, COSELM can save more than 50% time consumption.

ProCom: Designing and Evaluating a Mobile and Wearable System to Support Proximity Awareness for People with Autism

People with autism are at risk for social isolation due to differences in their perception and engagement with the social world. In this work, we aim to address one specific concern related to socialization the understanding, awareness, and use of interpersonal space. Over the course of a year, we iteratively designed and tested a series of concepts for supporting children with autism in perceiving, understanding, and responding to physical proximity with other people. During this process, we developed ProCom, a prototype system for measuring proximity without requiring instrumentation of the environment or another person. We used a variety of low and high fidelity prototypes, culminating in ProCom, to assess the feasibility, utility, and challenges of this approach. The results of these iterative design engagements indicate that wearable assistive technologies can support people in developing awareness of physical proximity in social settings. However, challenges related to both personal and collective use remain

Less Annotation on Personalized Activity Recognition Using Context Data

Miscellaneous mini-wearable devices (e.g. wristbands, smartwatches, armbands) have emerged in our life, capable of recognizing activities of daily living, monitoring health information, so on. Conventional activity recognition (AR) models deployed inside these devices are generic classifiers learned offline from abundant data. Transferring generic model to user-oriented model requires time-consuming human effort for annotations. To solve this problem, we propose SS-ARTMAP-AR, a self-supervised incremental learning AR model updated from surrounding information such as Bluetooth, Wi-Fi, GPS, GSM data without users annotation effort. Experimental results show that SS-ARTMAP-AR can gradually adapt individual users, become more incremental intelligence.

Leveraging Two-Stage Weighted ELM for Multimodal Wearables Based Fall Detection

For the elderly people, timely detecting the fall accident is very critical to receive the first aid. In order to achieve high detection accuracy and low false-alarm rate at the same time, we propose a multimodal wearables based fall detecting and monitoring method leveraging two-stage weighted extreme learning machine. Experimental results show that our method is able to effectively implement on miniaturized wearable devices, and compared to state-of-the-art ELM classifier, we can also obtain higher detection accuracy and lower false-alarm rate simultaneously, which enables various kinds of mHealth applications in large-scale population, especially for the elderly people’s healthcare in the field of fall detection.

AIR: recognizing activity through IR-based distance sensing on feet

In this paper, we describe the results of a controlled experiment measuring everyday movement activity through a novel recognition prototype named AIR. AIR measures distance from the feet using infrared (IR) sensors. We tested this approach for recognizing six prevalent activities: standing stationary, walking, running, walking in place, going upstairs, and going downstairs and compared results to other commonly used approaches. Our results show that AIR obtains much higher accuracy in recognizing activity than approaches that rely primarily on accelerometers. Moreover, AIR has good generalization ability when applying recognition model to new users.

OCEAN: a new opportunistic computing model for wearable activity recognition

Activities of Daily Living (ADL) recognition through wearable devices is an emerging research field. While, for many applications, recognition methods are faced with simultaneously dynamic changes in feature dimension, activity class and data distribution. Existing approaches mainly handle at most one of these three challenges, which significantly affects their performance. In this paper, we propose an Opportunistic Computing model for wEarable Activity recognitioN (OCEAN); by fusing random mapping, fuzzy clustering, and weight updating techniques, OCEAN can online adaptively adjust Single-hidden Layer Feedforward neural networks connection, structure and weight in a coherent manner. Experimental evaluations demonstrate that OCEAN improves the recognition accuracy by 5% to 15% compared to traditional approaches towards dynamic changes.

OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition

Miscellaneous mini-wearable devices (Jawbone Up, Apple Watch, Google Glass, et al.) have emerged in recent years to recognize the user’s activities of daily living (ADLs) such as walking, running, climbing and bicycling. To better suits a target user, a generic activity recognition (AR) model inside the wearable devices requires to adapt itself according to the user’s personality in terms of wearing styles and so on. In this paper, an online kernelized and regularized extreme learning machine (OKRELM) is proposed for wearable-based activity recognition. A small-scale but important subset of every incoming data chunk is chosen to go through the update stage during the online sequential learning. Therefore, OKRELM is a lightweight incremental learning model with less time consumption during the update and prediction phase, a robust and effective classifier compared with the batch learning scheme. The performance of OKRELM is evaluated and compared with several related approaches on a UCI online available AR dataset and experimental results show the efficiency and effectiveness of OKRELM.