/pdf/recognition-of-gait-patterns-in-older-adults-using-wearable-2csm5t2w.pdf

Recognition of Gait Patterns in Older Adults Using Wearable Smartwatch Devices: Observational Study

A high-performance mini-generator with average power of 2 W for human motion energy harvesting and wearable electronics applications

/pdf/fine-grained-motion-recognition-in-at-home-fitness-7nchgkq7.pdf

Fine-Grained Motion Recognition in At-Home Fitness Monitoring with Smartwatch: A Comparative Analysis of Explainable Deep Neural Networks

Advances in wearable and machine learning technologies have caused smartwatches to emerge as promising input devices across disciplines. In this study, we propose a user interface, designed for an automotive environment for recognizing input commands. As the use of extra elements to extend the scope of interactions may visually distract users during driving, we focus on using body parts as an interaction space. Specifically, we utilized the lap as an interaction hyperplane due to its near-flat surface, which provides users interaction analogous to a touchpad. To substantiate the proposed approach, we collected motion signals using an off-the-shelf smartwatch and trained them in supervised learning settings. As target gestures, we defined ten different hand gestures, such as tapping and folding/spreading all fingers on the lap. The experimental results exhibited a test accuracy of 94%, thereby validating the feasibility of the proposed approach.

Vehicle Control on an Uninstrumented Surface With an Off-the-Shelf Smartwatch

<sec> <title>BACKGROUND</title> Gait quality is generally considered a critical health indicator, especially for older adults. Assessing gait quality in an unobtrusive manner is essential for the seamless monitoring of these populations. However, precise evaluation of gait often requires dedicated sensor systems, which may not be feasible or practical for monitoring in everyday life. </sec> <sec> <title>OBJECTIVE</title> Gait quality is generally considered a critical health indicator, especially for older adults. Assessing gait quality in an unobtrusive manner is essential for the seamless monitoring of these populations. However, precise evaluation of gait often requires dedicated sensor systems, which may not be feasible or practical for monitoring in everyday life. </sec> <sec> <title>METHODS</title> We first defined four typical gait patterns of older adults: gait with disturbances, gait without disturbances, gait with cane, and gait with walker. Thereafter, we formulated the classification task of these gait patterns as a supervised machine learning problem in which multivariate time series signals of inertial motions captured while walking were learned and tested using deep neural network architectures in a supervised learning fashion. To demonstrate the feasibility of the proposed approach, we recruited 38 older adult participants (age: 80.4 +- 6.5; 73.7 % women). We also examined the internal representations and computational attention vectors learned by deep neural networks tailored to learn gait features from complex time series gait-related data. </sec> <sec> <title>RESULTS</title> The experimental results validated the feasibility of the proposed approach, revealing that 1) gait patterns of older adults were recognized using smartwatch measurements from both-hands with high accuracies (mean F_1-score of 97.4 %) and 2) either left- or right-hand measurement is sufficient for the proposed recognition task (mean F_1-score of 97.1 %). </sec> <sec> <title>CONCLUSIONS</title> The proposed wearable recognition system demonstrated acceptable performance (i.e., both accuracy and F_m were higher than 0.95), in predicting the gait patterns of the older adult population. </sec> 

Recognition of Gait Patterns in the Older Adults using Wearable Smartwatch Devices: Observational Study (Preprint)

People who exercise may benefit or be injured depending on their foot striking (FS) style. In this study, we propose an intelligent system that can recognize subtle differences in FS patterns while walking and running using measurements from a wearable smartwatch device. Although such patterns could be directly measured utilizing pressure distribution of feet while striking on the ground, we instead focused on analyzing hand movements by assuming that striking patterns consequently affect temporal movements of the whole body. The advantage of the proposed approach is that FS patterns can be estimated in a portable and less invasive manner. To this end, first, we developed a wearable system for measuring inertial movements of hands and then conducted an experiment where participants were asked to walk and run while wearing a smartwatch. Second, we trained and tested the captured multivariate time series signals in supervised learning settings. The experimental results obtained demonstrated high and robust classification performances (weighted-average F1 score > 90%) when recent deep neural network models, such as 1D-CNN and GRUs, were employed. We conclude this study with a discussion of potential future work and applications that increase benefits while walking and running properly using the proposed approach.

/pdf/estimation-of-fine-grained-foot-strike-patterns-with-1zftvh47.pdf

Estimation of Fine-Grained Foot Strike Patterns with Wearable Smartwatch Devices

This study aims to investigate how humans and artificial intelligence (AI) speakers interact and to examine the interactions based on three types of communication failures: system, semantic, and effectiveness. We divided service failures using AI speaker user data provided by the top telecommunication service providers in South Korea and investigated the means to increase the continuity of product use for each type. We proved the occurrence of failure due to system error (H1) and negative results on sustainable use of the AI speaker due to not understanding the meaning (H2). It was observed that the number of users increases as the effectiveness failure rate increases. For single-person households constituted by persons in their 30s and 70s or older, the continued use of AI speakers was significant. We found that it alleviated loneliness and that human-machine interaction using AI speaker could reach a high level through a high degree of meaning transfer. We also expect AI speakers to play a positive role in single-person households, especially in cases of the elderly, which has become a tough challenge in the recent times.

/pdf/toward-smart-communication-components-recent-advances-in-1hvz2ta7.pdf

Toward Smart Communication Components: Recent Advances in Human and AI Speaker Interaction

In this study, Based on the location information collected based on LTE Signal, we analyzed the changes in traffic flow by day of the week and the time zone in the greater Seoul metropolitan area (Seoul/Gyeonggi/Incheon). In the case of workers who commute to Seoul from Gyeonggi or Incheon, the time required to commute is about 66 minutes, which is about 1.4∼1.5 times higher than the 44∼46 minutes consumed by workers who commute within Seoul. The average speed of traffic in Seoul jurisdictional area was confirmed to be 31.0km/h. Those who live and work in the Seoul area took 45.2 minutes for going to work on Monday, and those who live in Gyeonggi or Incheon and work in Seoul took 69.2 minutes for going to work on Monday, which was the longest among all weekdays. In the analysis of weekday traffic speed in Seoul by time zones, it was the fastest at 35.1km/h at nighttime, followed by lunch time (32.1km/h), morning rush hour (29.3km/h), and quitting rush hour (28.6km/h). As a result of examining the change of traffic speed according to daytime time flow on weekdays, the peak of traffic congestion on morning rush hour was recorded at 29.4km/h at 08:30 and peak on quitting rush hour was 29.5km/h at 18:10. From 19:10 onwards, we can see that traffic congestion of quitting rush hour begins to ease. Likewise this study, the LTE signal-based movement and stay analysis can be used to construct lifelogs such as traffic, culture, and life patterns.

Hyejoo Kim

Papers

Recognition of Gait Patterns in Older Adults Using Wearable Smartwatch Devices: Observational Study

Estimation of Fine-Grained Foot Strike Patterns with Wearable Smartwatch Devices

Toward Smart Communication Components: Recent Advances in Human and AI Speaker Interaction

Analysis of movement and stay pattern using LTE signal: Focusing on changes of traffic conditions by day and time in Seoul city

Vehicle Control on an Uninstrumented Surface With an Off-the-Shelf Smartwatch