Using Smartphone Accelerometer for Human Physical Activity and Context Recognition in-the-Wild

Ehatisham-ul-Haq, Muhammad; Azam, Muhammad Awais; Asim, Yusra; Amin, Yasar; Naeem, Usman; Khalid, Asra

doi:10.1016/j.procs.2020.10.007

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…The most common factors examined were physical activity/mobility [ 2 , 3 , 10 , 11 , 22 , 23 , 49 , 50 , 51 , 52 , 53 ] and sociability [ 3 , 9 , 54 , 55 ]. Sensor data from the accelerometers, gyroscope, and location data (using GPS, Bluetooth) were used to infer factors such as places visited [ 50 ], amount of time spent at home [ 22 ], types of activity performed (sitting, standing, walking, running) [ 11 ] and regularity of movement [ 3 ].…”

Section: Resultsmentioning

confidence: 99%

“…For example, the Global Positioning System (GPS) data collected can provide a snapshot of the places a person visits in a day [ 7 , 8 , 9 ]. Combined with human activity recognition (“HAR”) algorithms, one could identify how the person commutes to these places—walking, running or by using a vehicle—as well as indicating their level of physical activity [ 3 , 10 , 11 ]. By combining the insights gained from all of the sensors, one can glean a range of insights about a person’s daily activities.…”

Section: Introductionmentioning

confidence: 99%

“…These capabilities, high user adoption, and the relatively unobtrusive nature of smartphones make them an appealing choice for ‘in-the-wild’ healthcare studies [ 3 , 10 , 12 , 13 ]. Several studies have utilized smartphones to remotely collect data from users and gain health insights from these data.…”

Section: Introductionmentioning

confidence: 99%

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Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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“…Therefore, signals are pre-processed before classification to eliminate the unwanted noise from raw data. [ 78 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Less is more: Efficient behavioral context recognition using Dissimilarity-Based Query Strategy

Akram¹,

Farhan²,

Basharat³

2023

PLoS ONE

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With the advancement of ubiquitous computing, smartphone sensors are generating a vast amount of unlabeled data streams ubiquitously. This sensor data can potentially help to recognize various behavioral contexts in the natural environment. Accurate behavioral context recognition has a wide variety of applications in many domains like disease prevention and independent living. However, despite the availability of enormous amounts of sensor data, label acquisition, due to its dependence on users, is still a challenging task. In this work, we propose a novel context recognition approach i.e., Dissimilarity-Based Query Strategy (DBQS). Our approach DBQS leverages Active Learning based selective sampling to find the informative and diverse samples in the sensor data to train the model. Our approach overcomes the stagnation problem by considering only new and distinct samples from the pool that were not previously explored. Further, our model exploits temporal information in the data in order to further maintain diversity in the dataset. The key intuition behind the proposed approach is that the variations during the learning phase will train the model in diverse settings and it will outperform when assigned a context recognition task in the natural setting. Experimentation on a publicly available natural environment dataset demonstrates that our proposed approach improved overall average Balanced Accuracy(BA) by 6% with an overall 13% less training data requirement.

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“…They evaluated model performance using six ML algorithms: Random Forest, Decision Tree, Bagging, K-Nearest Neighbor, Support Vector Machine, and Naive Bayes. The same approach was also used in [ 7 , 21 , 22 ].…”

Section: Related Workmentioning

confidence: 99%

A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition

Souza

Siqueira‐Silva

Andrade

et al. 2023

Sensors

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Adaptive AI for context and activity recognition remains a relatively unexplored field due to difficulty in collecting sufficient information to develop supervised models. Additionally, building a dataset for human context activities “in the wild” demands time and human resources, which explains the lack of public datasets available. Some of the available datasets for activity recognition were collected using wearable sensors, since they are less invasive than images and precisely capture a user’s movements in time series. However, frequency series contain more information about sensors’ signals. In this paper, we investigate the use of feature engineering to improve the performance of a Deep Learning model. Thus, we propose using Fast Fourier Transform algorithms to extract features from frequency series instead of time series. We evaluated our approach on the ExtraSensory and WISDM datasets. The results show that using Fast Fourier Transform algorithms to extract features performed better than using statistics measures to extract features from temporal series. Additionally, we examined the impact of individual sensors on identifying specific labels and proved that incorporating more sensors enhances the model’s effectiveness. On the ExtraSensory dataset, the use of frequency features outperformed that of time-domain features by 8.9 p.p., 0.2 p.p., 39.5 p.p., and 0.4 p.p. in Standing, Sitting, Lying Down, and Walking activities, respectively, and on the WISDM dataset, the model performance improved by 1.7 p.p., just by using feature engineering.

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Using Smartphone Accelerometer for Human Physical Activity and Context Recognition in-the-Wild

Cited by 13 publications

References 18 publications

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Less is more: Efficient behavioral context recognition using Dissimilarity-Based Query Strategy

A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition

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