Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification Article Swipe
Mahdi Pedram
,
Seyed Ali Rokni
,
Marjan Nourollahi
,
Houman Homayoun
,
Hassan Ghasemzadeh
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/bsn.2019.8771065
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/bsn.2019.8771065
Advances in embedded systems have enabled integration of many lightweight sensory devices within our daily life. In particular, this trend has given rise to continuous expansion of wearable sensors in a broad range of applications from health and fitness monitoring to social networking and military surveillance. Wearables leverage machine learning techniques to profile behavioral routine of their end-users through activity recognition algorithms. Current research assumes that such machine learning algorithms are trained offline. In reality, however, wearables demand continuous reconfiguration of their computational algorithms due to their highly dynamic operation. Developing a personalized and adaptive machine learning model requires real-time reconfiguration of the model. Due to stringent computation and memory constraints of these embedded sensors, the training/re-training of the computational algorithms need to be memory- and computation-efficient. In this paper, we propose a framework, based on the notion of online learning, for real-time and on-device machine learning training. We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers. Our results, based on Pegasos online learning, demonstrate that the proposed approach achieves 97% accuracy in detecting activities of varying intensities using a limited memory while power usages of the system is reduced by more than 40%.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/bsn.2019.8771065
- OA Status
- green
- Cited By
- 1
- References
- 8
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2955272984Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/bsn.2019.8771065Digital Object Identifier
- Title
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Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary ClassificationWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-05-01Full publication date if available
- Authors
-
Mahdi Pedram, Seyed Ali Rokni, Marjan Nourollahi, Houman Homayoun, Hassan GhasemzadehList of authors in order
- Landing page
-
https://doi.org/10.1109/bsn.2019.8771065Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1907.03250Direct OA link when available
- Concepts
-
Computer science, Machine learning, Wearable computer, Artificial intelligence, Leverage (statistics), Control reconfiguration, Activity recognition, Computation, Embedded system, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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8Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.notion | 137 |
| abstract_inverted_index.online | 139, 170, 178 |
| abstract_inverted_index.paper, | 129 |
| abstract_inverted_index.social | 41 |
| abstract_inverted_index.system | 203 |
| abstract_inverted_index.usages | 200 |
| abstract_inverted_index.within | 12 |
| abstract_inverted_index.Current | 62 |
| abstract_inverted_index.Pegasos | 177 |
| abstract_inverted_index.assumes | 64 |
| abstract_inverted_index.devices | 11 |
| abstract_inverted_index.dynamic | 88 |
| abstract_inverted_index.enabled | 5 |
| abstract_inverted_index.fitness | 38 |
| abstract_inverted_index.limited | 196 |
| abstract_inverted_index.machine | 48, 67, 95, 145 |
| abstract_inverted_index.memory- | 124 |
| abstract_inverted_index.problem | 155, 160 |
| abstract_inverted_index.profile | 52 |
| abstract_inverted_index.propose | 131, 149 |
| abstract_inverted_index.reduced | 205 |
| abstract_inverted_index.routine | 54 |
| abstract_inverted_index.sensors | 28 |
| abstract_inverted_index.sensory | 10 |
| abstract_inverted_index.systems | 3 |
| abstract_inverted_index.through | 58 |
| abstract_inverted_index.trained | 71 |
| abstract_inverted_index.varying | 192 |
| abstract_inverted_index.Advances | 0 |
| abstract_inverted_index.accuracy | 187 |
| abstract_inverted_index.achieves | 185 |
| abstract_inverted_index.activity | 59, 153 |
| abstract_inverted_index.adaptive | 94 |
| abstract_inverted_index.approach | 184 |
| abstract_inverted_index.embedded | 2, 113 |
| abstract_inverted_index.however, | 75 |
| abstract_inverted_index.learning | 49, 68, 96, 146 |
| abstract_inverted_index.leverage | 47 |
| abstract_inverted_index.military | 44 |
| abstract_inverted_index.offline. | 72 |
| abstract_inverted_index.proposed | 183 |
| abstract_inverted_index.reality, | 74 |
| abstract_inverted_index.requires | 98 |
| abstract_inverted_index.research | 63 |
| abstract_inverted_index.results, | 174 |
| abstract_inverted_index.sensors, | 114 |
| abstract_inverted_index.wearable | 27 |
| abstract_inverted_index.Wearables | 46 |
| abstract_inverted_index.cascading | 169 |
| abstract_inverted_index.decisions | 167 |
| abstract_inverted_index.detecting | 189 |
| abstract_inverted_index.end-users | 57 |
| abstract_inverted_index.expansion | 25 |
| abstract_inverted_index.learning, | 140, 179 |
| abstract_inverted_index.on-device | 144 |
| abstract_inverted_index.real-time | 99, 142 |
| abstract_inverted_index.stringent | 106 |
| abstract_inverted_index.training. | 147 |
| abstract_inverted_index.transform | 151 |
| abstract_inverted_index.wearables | 76 |
| abstract_inverted_index.Developing | 90 |
| abstract_inverted_index.activities | 190 |
| abstract_inverted_index.algorithms | 69, 83, 120 |
| abstract_inverted_index.behavioral | 53 |
| abstract_inverted_index.continuous | 24, 78 |
| abstract_inverted_index.framework, | 133 |
| abstract_inverted_index.monitoring | 39 |
| abstract_inverted_index.networking | 42 |
| abstract_inverted_index.operation. | 89 |
| abstract_inverted_index.techniques | 50 |
| abstract_inverted_index.algorithms. | 61 |
| abstract_inverted_index.computation | 107 |
| abstract_inverted_index.constraints | 110 |
| abstract_inverted_index.demonstrate | 180 |
| abstract_inverted_index.integration | 6 |
| abstract_inverted_index.intensities | 193 |
| abstract_inverted_index.lightweight | 9 |
| abstract_inverted_index.multi-class | 158 |
| abstract_inverted_index.particular, | 17 |
| abstract_inverted_index.recognition | 60, 154 |
| abstract_inverted_index.applications | 34 |
| abstract_inverted_index.classifiers. | 172 |
| abstract_inverted_index.hierarchical | 163 |
| abstract_inverted_index.personalized | 92 |
| abstract_inverted_index.computational | 82, 119 |
| abstract_inverted_index.surveillance. | 45 |
| abstract_inverted_index.classification | 159 |
| abstract_inverted_index.reconfiguration | 79, 100 |
| abstract_inverted_index.training/re-training | 116 |
| abstract_inverted_index.computation-efficient. | 126 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.05497128 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |