This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

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From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Background: Widespread diffusion of mobile phone and Web 2.0 technologies make them potentially useful tools for promoting health and tackling public health issues, such as the increasing prevalence of overweight and obesity. Research in this domain is growing rapidly but, to date, no review has comprehensively and systematically documented how mobile and Web 2.0 technologies are being deployed and evaluated in relation to weight management. Objective: To provide an up-to-date, comprehensive map of the literature discussing the use of mobile phone and Web 2.0 apps for influencing behaviors related to weight management (ie, diet, physical activity [PA], weight control, etc). Methods: A systematic scoping review of the literature was conducted based on a published protocol (registered at PROSPERO: CRD42014010323). Using a comprehensive search strategy, we searched 16 multidisciplinary electronic databases for original research documents published in English between 2004 and 2014. We used duplicate study selection and data extraction. Using an inductively developed charting tool, selected articles were thematically categorized. Results: We identified 457 articles, mostly published between 2013 and 2014 in 157 different journals and 89 conference proceedings. Articles were categorized around two overarching themes, which described the use of technologies for either (1) promoting behavior change (309/457, 67.6%) or (2) measuring behavior (103/457, 22.5%). The remaining articles were overviews of apps and social media content (33/457, 7.2%) or covered a combination of these three themes (12/457, 2.6%). Within the two main overarching themes, we categorized articles as representing three phases of research development: (1) design and development, (2) feasibility studies, and (3) evaluations. Overall, articles mostly reported on evaluations of technologies for behavior change (211/457, 46.2%). Conclusions: There is an extensive body of research on mobile phone and Web 2.0 technologies for weight management. Research has reported on (1) the development, feasibility, and efficacy of persuasive mobile technologies used in interventions for behavior change (PA and diet) and (2) the design, feasibility, and accuracy of mobile phone apps for behavioral assessment. Further research has focused exclusively on analyses of the content and quality of available apps. Limited evidence exists on the use of social media for behavior change, but a segment of studies deal with content analyses of social media. Future research should analyze mobile phone and Web 2.0 technologies together by combining the evaluation of content and design aspects with usability, feasibility, and efficacy/effectiveness for behavior change, or accuracy/validity for behavior assessment, in order to understand which technological components and features are likely to result in effective interventions. [J Med Internet Res 2015;17(11):e259]

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Mobile Phone and Web 2.0 Technologies for Weight Management: A Systematic Scoping Review.

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments For subjects with normal gait, the best performing model systematically overestimates speed by only 001 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 012 m/s Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 001 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 014 m/s Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment

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A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis.

Walking speed is widely used to study human health status. Wearable inertial measurement units (IMU) are promising tools for the ambulatory measurement of walking speed. Among wearable inertial sensors, the ones worn on the wrist, such as a watch or band, have relatively higher potential to be easily incorporated into daily lifestyle. Using the arm swing motion in walking, this paper proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU. A novel kinematic variable is proposed, which finds the wrist acceleration in the principal axis (i.e. the direction of the arm swing). This variable (called pca-acc) is obtained by applying sensor fusion on IMU data to find the orientation followed by the use of principal component analysis. An experimental evaluation was performed on 15 healthy young subjects during free walking trials. The experimental results show that the use of the proposed pca-acc variable can significantly improve the walking speed estimation accuracy when compared to the use of raw acceleration information (p<0.01). When Gaussian process regression is used, the resulting walking speed estimation accuracy and precision is about 5.9% and 4.7%, respectively.

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Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor.

https://ubibliorum.ubi.pt/bitstream/10400.6/8263/1/2018%20-%20Identification%20of%20activities%20of%20daily%20living%20through%20data%20fusion%20on%20motion%20and%20magnetic%20sensors%20embedded%20on%20mobile%20devices.pdf

Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices

Activity recognition for human behavior monitoring is an important research topic in the field of mHealth, especially for aspects of physical activity linked to fitness and disease progress, such as walking and walking speed. Sensors embedded into smartphones recently enabled new opportunities for non invasive activity and walking speed inference. In this paper, we propose a data fusion approach to the problem of physical activity recognition and walking speed estimation using smartphones. Our architecture combines different sensors to take into account practical issues arising in realistic settings, such as variability in phone location and orientation. Additionally, we introduce a novel automatic calibration methodology combining accelerometer and GPS data while walking in unconstrained settings, in order to reduce walking speed estimation error at the individual level. The proposed system was validated in 20 participants while performing sedentary, household, ambulatory and sport activities, in both indoor laboratory and outdoor self-paced settings. We show that by combining accelerometer and gyroscope data, smartphone location can be distinguished between the two most commonly used positions (bag and pocket), regardless of phone orientation (97 % f-score). Location-specific activity recognition models can significantly improve activity recognition performance (p = 0.0010 < α), especially helping in distinguishing activities involving similar motion patterns (91 % f-score overall, improvements between 4% and 11 % for walking and biking activities). Our proposed method to personalize walking speed estimates, by automatically calibrating walking speed estimation models during a short self-paced walk, reduced walking speed estimation error by 8.8% on average (p = 0.0012 < α).

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Marijn van Dort

Papers

Self-calibration of walking speed estimations using smartphone sensors