From 32e47b7fde4aa4144ce0b43a3c6f3678428c1055 Mon Sep 17 00:00:00 2001 From: BoyangW Date: Fri, 1 Nov 2024 05:03:19 +0000 Subject: [PATCH] deploy: 0174e829cf00fd88c4cbfdebc633d195b4370cf9 --- CNAME | 1 - categories/index.html | 64 +++++++++++++++++------------------ grants/eat/index.html | 64 +++++++++++++++++++++++++++++++---- grants/index.html | 12 +++---- grants/index.xml | 16 ++++----- img/Angela.jpg | Bin 0 -> 3843 bytes img/Annie.jpg | Bin 0 -> 37611 bytes img/Lucia.jpg | Bin 0 -> 4439 bytes img/Lucia.png | Bin 0 -> 17992 bytes index.html | 66 ++++++++++++++++++------------------- index.xml | 41 +++++++++++++++++------ news/New Text Document.txt | 0 news/index.html | 39 ++++++++++++++++++++++ news/index.xml | 23 ++++++++++++- profiles/boyang/index.html | 5 +++ profiles/index.xml | 2 +- sitemap.xml | 18 +++++++--- tags/index.html | 64 +++++++++++++++++------------------ 18 files changed, 281 insertions(+), 134 deletions(-) delete mode 100644 CNAME create mode 100644 img/Angela.jpg create mode 100644 img/Annie.jpg create mode 100644 img/Lucia.jpg create mode 100644 img/Lucia.png create mode 100644 news/New Text Document.txt diff --git a/CNAME b/CNAME deleted file mode 100644 index deae5e83..00000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -www.thehabitslab.com \ No newline at end of file diff --git a/categories/index.html b/categories/index.html index bfd1d157..83243c8e 100644 --- a/categories/index.html +++ b/categories/index.html @@ -304,6 +304,19 @@

Recent News +
+ Oct 14
+ 2024 +
+
+ + Farzad's paper got accepted by Nature Digital Medicine 2024 + +

A machine-learned model for predicting weight loss success using weight change features early in treatment

+
+ +
Jun 4
@@ -317,6 +330,19 @@

Recent News

+
+
+ Nov 1
+ 2023 +
+
+ + 5 SBM abstracts got accepted from HABits Lab 2024 + +

Alshurafa and the HABits Lab presented their findings from Alshurafa's K25, R03, and R21 studies at the Society of Behavioral Medicine. We received Meritorious and Citation awards, as well as a Live Research Spotlight.

+
+
+
Oct 10
@@ -382,32 +408,6 @@

Recent News

-
-
- May 1
- 2023 -
-
- - HABits Lab partners with Palbud Inc. on autism wearable project - -

HABits Lab will work with Palbud to pursue SBIR Phase 2 funding

-
-
- -
-
- Apr 9
- 2023 -
-
- - Glenn Fernandes' paper on cartoonized life-vlogging accepted by ACM SIGCHI - -

Is cartoonized life-vlogging the key to increasing adoption of activity-oriented wearable camera systems?

-
-
-
Grants

- +
- SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing -

$850,000 / PI: Nabil Alshurafa / 2018 - 2023

+ EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models +

$299,471 / PI: Nabil Alshurafa / 2019 - 2022

- +
- EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models -

$299,471 / PI: Nabil Alshurafa / 2019 - 2022

+ SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing +

$850,000 / PI: Nabil Alshurafa / 2018 - 2023

diff --git a/grants/eat/index.html b/grants/eat/index.html index 70a170c0..c9c0e368 100644 --- a/grants/eat/index.html +++ b/grants/eat/index.html @@ -132,8 +132,8 @@

EAT: A Reliable Eating Assessment Technology for Free-living Individuals

1. Introduction

-

Wearable cameras are used as a tool to understand fine-grained human activities in the wild because of their ability to provide visual information that can be interpreted by humans [15, 45, 55] or machines [6, 43, 48]. Particularly in the ubiquitous computing (UbiComp) community, wearable cameras are increasingly being used to obtain visually confirmed annotations of wearers’ activities in real-world settings, which is necessary to both understand human.

-

Behavior at a fine-grained level, and build and validate non-visual wearable devices and their corresponding supervised machine learning algorithms to automate the detection of human activity [4, 8, 9, 61, 80]. However, the stream of images obtained from these wearable cameras embeds more details than needed

+

Wearable cameras are used as a tool to understand fine-grained human activities in the wild because of their ability to provide visual information that can be interpreted by humans or machines. Particularly in the ubiquitous computing (UbiComp) community, wearable cameras are increasingly being used to obtain visually confirmed annotations of wearers’ activities in real-world settings, which is necessary to both understand human.

+

Behavior at a fine-grained level, and build and validate non-visual wearable devices and their corresponding supervised machine learning algorithms to automate the detection of human activity. However, the stream of images obtained from these wearable cameras embeds more details than needed

In particular, we want to compare the accuracy of human labels obtained from viewing non-obfuscated videos with the accuracy of the labels derived from viewing the obfuscated videos with different filters. Hand-to-head gestures can be confounding to each other if fine-grained and some contextual information is lost. Therefore, this comparison can help us to determine if the visual confirmation utility is preserved, or not, after applying activity-oriented partial obfuscation to it with different filters. It will also help us to understand the limitations of activity-oriented partial obfuscation and the filters applied.

Example image

@@ -165,14 +165,66 @@

Co-investigator

+ image here +
+

Angela Fidler Pfammatter
+ Adjunct Associate Professor of Preventive Medicine (Behavioral Medicine) +

+ + Profile + +
+ +
+
+ image here +
+

Annie W. Lin
+ Assistant Professor of Nutrition Informatics, Hormel Institute, University of Minnesota +

+ + Profile + +
+ +
+
+ image here +
+

Josiah Hester
+ Associate Professor of Interactive Computing and Computer Science College of Computing, Georgia Institute of Technology +

+ + Profile + +
+ +
+
+ image here +
+

Lucia C Petito
+ Assistant Professor of Preventive Medicine (Biostatistics and Informatics) +

+ + Profile + +
+ +
+
image here
-

Name1
+

Blaine Rothrock
Researcher

Profile + href=https://blainerothrock.com/>Profile
@@ -180,12 +232,12 @@

Co-investigator

image here
-

Name2
+

Soroush Shahi
Researcher

Profile + href=https://www.thehabitslab.com/profiles/soroush/>Profile diff --git a/grants/index.html b/grants/index.html index d92f79ff..6730d306 100644 --- a/grants/index.html +++ b/grants/index.html @@ -236,21 +236,21 @@

Grants

- +
- SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing -

$850,000 / PI: Nabil Alshurafa / 2018 - 2023

+ EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models +

$299,471 / PI: Nabil Alshurafa / 2019 - 2022

- +
- EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models -

$299,471 / PI: Nabil Alshurafa / 2019 - 2022

+ SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing +

$850,000 / PI: Nabil Alshurafa / 2018 - 2023

diff --git a/grants/index.xml b/grants/index.xml index d3b696de..f4ff2602 100644 --- a/grants/index.xml +++ b/grants/index.xml @@ -13,7 +13,7 @@ https://HAbitsLab.github.io/grants/eat/ Tue, 27 Jul 2021 15:09:31 -0600 https://HAbitsLab.github.io/grants/eat/ - <h3 id="1-introduction">1. Introduction</h3> <p>Wearable cameras are used as a tool to understand fine-grained human activities in the wild because of their ability to provide visual information that can be interpreted by humans [15, 45, 55] or machines [6, 43, 48]. Particularly in the ubiquitous computing (UbiComp) community, wearable cameras are increasingly being used to obtain visually confirmed annotations of wearers’ activities in real-world settings, which is necessary to both understand human.</p> + <h3 id="1-introduction">1. Introduction</h3> <p>Wearable cameras are used as a tool to understand fine-grained human activities in the wild because of their ability to provide visual information that can be interpreted by humans or machines. Particularly in the ubiquitous computing (UbiComp) community, wearable cameras are increasingly being used to obtain visually confirmed annotations of wearers’ activities in real-world settings, which is necessary to both understand human.</p> <p>Behavior at a fine-grained level, and build and validate non-visual wearable devices and their corresponding supervised machine learning algorithms to automate the detection of human activity. However, the stream of images obtained from these wearable cameras embeds more details than needed</p> WildCam: A Privacy Conscious Wearable Eating Detection Camera People will Actually Wear in the Wild @@ -29,6 +29,13 @@ https://HAbitsLab.github.io/grants/behaviorsight/ <h3 id="introduction">Introduction</h3> <p>Many health-risk behaviors—such as overeating, smoking, drinking, substance abuse, and medication non-adherence—correlate with increased morbidity and mortality. Eliminating these behaviors can help prevent several diseases. However, this requires understanding, knowing, and altering what people put into their mouth. Current efforts to understand behaviors associated with detecting what people put in their mouth are limited and rely on inaccurate and biased self-reports. Detecting these behaviors objectively and in real time, learning to automatically predict them, and adaptively intervening to problematic behaviors will pave the way for novel behavioral interventions.</p> + + EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models + https://HAbitsLab.github.io/grants/nsf-eager-/ + Wed, 05 Jun 2019 15:09:31 -0600 + https://HAbitsLab.github.io/grants/nsf-eager-/ + <h3 id="introduction">Introduction</h3> <p>Studying human behavior has traditionally been theory-based, deriving a set of constructs or complex concepts (e.g., self-efficacy or attitude towards behavior) and relationships between variables to explain or predict behavior and then subjecting components of the theory to empirical testing. Ecological momentary assessments (EMAs) allow participants to report behavior when events occur using their smartphones. However, they are still prone to bias and prevent contextual understanding of behavior in free-living settings. Wearable mobile sensors enable quantitative representation of coarse (e.g., a person eating alone or with friends) and fine-grained behaviors (e.g., number of feeding gestures), facilitating the development of new behavioral models that explain and predict problematic behavior such as overeating. This will allow the behaviorist to construct models that more closely represent reality and enable the improved design of interventions that change behavior. However, building models of these behaviors requires a reliable, consistent, and constant source of ground truth for development and verification. Researchers can automatically uncover contextual information about human behavior in their natural habitat using wearable cameras combined with IR-sensor arrays and advanced computational methods. However, wearers&rsquo; perception of the bystanders&rsquo; privacy concerns is reported to be the leading reason people are not willing to wear cameras in daily life continuously.</p> + SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing https://HAbitsLab.github.io/grants/sensewhy/ @@ -36,12 +43,5 @@ https://HAbitsLab.github.io/grants/sensewhy/ <h3 id="1-introduction">1. Introduction</h3> <p>Obesity, caused primarily by overeating relative to need, is a preventable chronic disease that affects 42.4% of US adults, increasing their risk of cardiovascular disease. Obesity also exacts staggering costs on the US healthcare system.</p> <p>A challenge in treating obesity is that, unlike substance abuse, the treatment goal cannot be simply to extinguish the ingestive behavior, as eating is necessary to sustain life. Obesity treatment would benefit greatly from an ability to discriminate between and intervene upon the specific problematic eating behavioral phenotypes that foster obesity.</p> - - EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models - https://HAbitsLab.github.io/grants/nsf-eager-/ - Mon, 01 Jan 0001 00:00:00 +0000 - https://HAbitsLab.github.io/grants/nsf-eager-/ - <h3 id="introduction">Introduction</h3> <p>Studying human behavior has traditionally been theory-based, deriving a set of constructs or complex concepts (e.g., self-efficacy or attitude towards behavior) and relationships between variables to explain or predict behavior and then subjecting components of the theory to empirical testing. Ecological momentary assessments (EMAs) allow participants to report behavior when events occur using their smartphones. However, they are still prone to bias and prevent contextual understanding of behavior in free-living settings. Wearable mobile sensors enable quantitative representation of coarse (e.g., a person eating alone or with friends) and fine-grained behaviors (e.g., number of feeding gestures), facilitating the development of new behavioral models that explain and predict problematic behavior such as overeating. This will allow the behaviorist to construct models that more closely represent reality and enable the improved design of interventions that change behavior. However, building models of these behaviors requires a reliable, consistent, and constant source of ground truth for development and verification. Researchers can automatically uncover contextual information about human behavior in their natural habitat using wearable cameras combined with IR-sensor arrays and advanced computational methods. However, wearers&rsquo; perception of the bystanders&rsquo; privacy concerns is reported to be the leading reason people are not willing to wear cameras in daily life continuously.</p> - diff --git a/img/Angela.jpg b/img/Angela.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a62cb5687977b6104f810dd0a1a1c358b4c42efe GIT binary patch literal 3843 zcmb7Bc{tSX+x^VKjGaMQ#voh9(u}2SBeKL~Ft*B;C2Ns2dyV8iWLF-}V0ap8JpIT+e;(^W4{Y_DA+-0A9SYsWAWogMq*J?+pk9 zm>d+aKMRnI!hJjefItAy2O>DY0zv?g0|q)M03-si{N(_E2Z{cNTY--Lt2uDK0*ha$v`!9g~XQm`Nyei`{RHC0B2=|v9iEnFc=#foSjpEi<5(cQy9U|BOoRs zjuI0=A|<4iW-9$OUpCpd$d74+P-@?Y9BQgA7HvteR9}7YftIx_$bUmUH7%PyPS0$)=ss$!xtgU%$W4g1~$bNd%T(AF6W7HE)0NAbi>VF@O^SI#?9K2b=^JtOvBHY!o(u z#P(*K+-p7P=H?N0jhis+C<&zYb55-^4J))@gYZ0|Zy@0QaIx&pP@(;U(;7%PBLoIb z3+C(eK;VaskzSk0ol;VTo7Mt-0jlJxdH?UaTbf+Wm(Lx--`zy++;CHJtQ}^t$V)78 zQ#T|@2^mh}8ZtY;p(KxVd>kvi4Y*Ysg*25VyG&&dv;o&lR#8YdDNmbjR^d}5F}RJ66g2pJ96BPx4O`lm|ed^tvsdm 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Recent News +
+ Oct 14
+ 2024 +
+
+ + Farzad's paper got accepted by Nature Digital Medicine 2024 + +

A machine-learned model for predicting weight loss success using weight change features early in treatment

+
+ +
Jun 4
@@ -347,6 +360,19 @@

Recent News

+
+
+ Nov 1
+ 2023 +
+
+ + 5 SBM abstracts got accepted from HABits Lab 2024 + +

Alshurafa and the HABits Lab presented their findings from Alshurafa's K25, R03, and R21 studies at the Society of Behavioral Medicine. We received Meritorious and Citation awards, as well as a Live Research Spotlight.

+
+
+
Oct 10
@@ -412,32 +438,6 @@

Recent News

-
-
- May 1
- 2023 -
-
- - HABits Lab partners with Palbud Inc. on autism wearable project - -

HABits Lab will work with Palbud to pursue SBIR Phase 2 funding

-
-
- -
-
- Apr 9
- 2023 -
-
- - Glenn Fernandes' paper on cartoonized life-vlogging accepted by ACM SIGCHI - -

Is cartoonized life-vlogging the key to increasing adoption of activity-oriented wearable camera systems?

-
-
-
Grants

- +
- SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing -

$850,000 / PI: Nabil Alshurafa / 2018 - 2023

+ EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models +

$299,471 / PI: Nabil Alshurafa / 2019 - 2022

- +
- EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models -

$299,471 / PI: Nabil Alshurafa / 2019 - 2022

+ SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing +

$850,000 / PI: Nabil Alshurafa / 2018 - 2023

diff --git a/index.xml b/index.xml index 3ace7355..3fd7574f 100644 --- a/index.xml +++ b/index.xml @@ -6,8 +6,15 @@ Recent content in Homepage on HabitsLab Hugo en-us - Wed, 11 Sep 2024 15:09:31 -0600 + Mon, 14 Oct 2024 15:09:31 -0600 + + Farzad's paper got accepted by Nature Digital Medicine 2024 + https://HAbitsLab.github.io/news/farzad_nature/ + Mon, 14 Oct 2024 15:09:31 -0600 + https://HAbitsLab.github.io/news/farzad_nature/ + + NIR-sighted: A Programmable Streaming Architecture for Low-Energy Human-Centric Vision Applications https://HAbitsLab.github.io/publications/nirsighted/ @@ -29,6 +36,13 @@ https://HAbitsLab.github.io/news/shibosensors/ + + 5 SBM abstracts got accepted from HABits Lab 2024 + https://HAbitsLab.github.io/news/sbm2024/ + Wed, 01 Nov 2023 15:09:31 -0600 + https://HAbitsLab.github.io/news/sbm2024/ + + Dr. Alshurafa presents on SmokeMon at UbiComp/ISWC 2023 https://HAbitsLab.github.io/news/ubicomp23/ @@ -148,6 +162,13 @@ https://HAbitsLab.github.io/news/wgn9smokemon/ + + Boyang Wei's paper accepted by IEEE Journal of Biomedical and Health Informatics 2023 + https://HAbitsLab.github.io/news/jbhi_boyang/ + Sat, 04 Feb 2023 15:09:31 -0600 + https://HAbitsLab.github.io/news/jbhi_boyang/ + + SmokeMon: Unobtrusive Extraction of Smoking Topography Using Wearable Energy-Efficient Thermal https://HAbitsLab.github.io/publications/smokemon/ @@ -258,7 +279,7 @@ https://HAbitsLab.github.io/profiles/boyang/ Thu, 30 Dec 2021 15:09:31 -0600 https://HAbitsLab.github.io/profiles/boyang/ - <h3 id="education">Education</h3> <p>M.S. Analytics, Georgetown University</p> <p>B.S. Biochemistry, University of Washington</p> + <h3 id="education">Education</h3> <p>M.S. Analytics, Georgetown University</p> <p>B.S. Biochemistry, University of Washington</p> <h3 id="research">Research</h3> <p>My line of research focuses on building resource-constrained ML models for behavioral and physical activities recognition from wearables.</p> <p>I have a mixed background of Biochemistry, Computer Science and Analytics. My research interests range from machine learning, data analytics to mHealth and passive sensing.</p> <h3 id="website">Website</h3> <p><a href="https://boyangw.com/">https://boyangw.com/</a></p> Chris Romano @@ -426,7 +447,7 @@ https://HAbitsLab.github.io/grants/eat/ Tue, 27 Jul 2021 15:09:31 -0600 https://HAbitsLab.github.io/grants/eat/ - <h3 id="1-introduction">1. Introduction</h3> <p>Wearable cameras are used as a tool to understand fine-grained human activities in the wild because of their ability to provide visual information that can be interpreted by humans [15, 45, 55] or machines [6, 43, 48]. Particularly in the ubiquitous computing (UbiComp) community, wearable cameras are increasingly being used to obtain visually confirmed annotations of wearers’ activities in real-world settings, which is necessary to both understand human.</p> + <h3 id="1-introduction">1. Introduction</h3> <p>Wearable cameras are used as a tool to understand fine-grained human activities in the wild because of their ability to provide visual information that can be interpreted by humans or machines. Particularly in the ubiquitous computing (UbiComp) community, wearable cameras are increasingly being used to obtain visually confirmed annotations of wearers’ activities in real-world settings, which is necessary to both understand human.</p> <p>Behavior at a fine-grained level, and build and validate non-visual wearable devices and their corresponding supervised machine learning algorithms to automate the detection of human activity. However, the stream of images obtained from these wearable cameras embeds more details than needed</p> Moving the dial on prenatal stress mechanisms of neurodevelopmental vulnerability to mental health problems: A personalized prevention proof of concept @@ -652,6 +673,13 @@ https://HAbitsLab.github.io/news/news1/ + + EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models + https://HAbitsLab.github.io/grants/nsf-eager-/ + Wed, 05 Jun 2019 15:09:31 -0600 + https://HAbitsLab.github.io/grants/nsf-eager-/ + <h3 id="introduction">Introduction</h3> <p>Studying human behavior has traditionally been theory-based, deriving a set of constructs or complex concepts (e.g., self-efficacy or attitude towards behavior) and relationships between variables to explain or predict behavior and then subjecting components of the theory to empirical testing. Ecological momentary assessments (EMAs) allow participants to report behavior when events occur using their smartphones. However, they are still prone to bias and prevent contextual understanding of behavior in free-living settings. Wearable mobile sensors enable quantitative representation of coarse (e.g., a person eating alone or with friends) and fine-grained behaviors (e.g., number of feeding gestures), facilitating the development of new behavioral models that explain and predict problematic behavior such as overeating. This will allow the behaviorist to construct models that more closely represent reality and enable the improved design of interventions that change behavior. However, building models of these behaviors requires a reliable, consistent, and constant source of ground truth for development and verification. Researchers can automatically uncover contextual information about human behavior in their natural habitat using wearable cameras combined with IR-sensor arrays and advanced computational methods. However, wearers&rsquo; perception of the bystanders&rsquo; privacy concerns is reported to be the leading reason people are not willing to wear cameras in daily life continuously.</p> + Dr. Alshurafa receives NSF EAGER Award https://HAbitsLab.github.io/news/news2/ @@ -820,12 +848,5 @@ https://HAbitsLab.github.io/publications/opportunistic-hierarchical-classification/ <h3 id="1-introduction">1. Introduction</h3> <p>Wearable cameras are used as a tool to understand fine-grained human activities in the wild because of their ability to provide visual information that can be interpreted by humans [15, 45, 55] or machines [6, 43, 48]. Particularly in the ubiquitous computing (UbiComp) community, wearable cameras are increasingly being used to obtain visually confirmed annotations of wearers’ activities in real-world settings, which is necessary to both understand human.</p> - - EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models - https://HAbitsLab.github.io/grants/nsf-eager-/ - Mon, 01 Jan 0001 00:00:00 +0000 - https://HAbitsLab.github.io/grants/nsf-eager-/ - <h3 id="introduction">Introduction</h3> <p>Studying human behavior has traditionally been theory-based, deriving a set of constructs or complex concepts (e.g., self-efficacy or attitude towards behavior) and relationships between variables to explain or predict behavior and then subjecting components of the theory to empirical testing. Ecological momentary assessments (EMAs) allow participants to report behavior when events occur using their smartphones. However, they are still prone to bias and prevent contextual understanding of behavior in free-living settings. Wearable mobile sensors enable quantitative representation of coarse (e.g., a person eating alone or with friends) and fine-grained behaviors (e.g., number of feeding gestures), facilitating the development of new behavioral models that explain and predict problematic behavior such as overeating. This will allow the behaviorist to construct models that more closely represent reality and enable the improved design of interventions that change behavior. However, building models of these behaviors requires a reliable, consistent, and constant source of ground truth for development and verification. Researchers can automatically uncover contextual information about human behavior in their natural habitat using wearable cameras combined with IR-sensor arrays and advanced computational methods. However, wearers&rsquo; perception of the bystanders&rsquo; privacy concerns is reported to be the leading reason people are not willing to wear cameras in daily life continuously.</p> - diff --git a/news/New Text Document.txt b/news/New Text Document.txt new file mode 100644 index 00000000..e69de29b diff --git a/news/index.html b/news/index.html index 73969626..cc984c37 100644 --- a/news/index.html +++ b/news/index.html @@ -176,6 +176,19 @@

Recent News +
+ Oct 14
+ 2024 +
+
+ + Farzad's paper got accepted by Nature Digital Medicine 2024 + +

A machine-learned model for predicting weight loss success using weight change features early in treatment

+
+ +
Jun 4
@@ -189,6 +202,19 @@

Recent News

+
+
+ Nov 1
+ 2023 +
+
+ + 5 SBM abstracts got accepted from HABits Lab 2024 + +

Alshurafa and the HABits Lab presented their findings from Alshurafa's K25, R03, and R21 studies at the Society of Behavioral Medicine. We received Meritorious and Citation awards, as well as a Live Research Spotlight.

+
+
+
Oct 10
@@ -319,6 +345,19 @@

Recent News

+
+
+ Feb 4
+ 2023 +
+
+ + Boyang Wei's paper accepted by IEEE Journal of Biomedical and Health Informatics 2023 + +

An End-to-end Energy-efficient Approach for Intake Detection With Low Inference Time Using Wrist-worn Sensor

+
+
+
Jan 1
diff --git a/news/index.xml b/news/index.xml index fc4ca670..90889ae1 100644 --- a/news/index.xml +++ b/news/index.xml @@ -6,8 +6,15 @@ Recent content in News on HabitsLab Hugo en-us - Tue, 04 Jun 2024 12:09:31 -0600 + Mon, 14 Oct 2024 15:09:31 -0600 + + Farzad's paper got accepted by Nature Digital Medicine 2024 + https://HAbitsLab.github.io/news/farzad_nature/ + Mon, 14 Oct 2024 15:09:31 -0600 + https://HAbitsLab.github.io/news/farzad_nature/ + + HABits Lab Alum Shibo Zhang wins MDPI Sensors Best Paper Award https://HAbitsLab.github.io/news/shibosensors/ @@ -15,6 +22,13 @@ https://HAbitsLab.github.io/news/shibosensors/ + + 5 SBM abstracts got accepted from HABits Lab 2024 + https://HAbitsLab.github.io/news/sbm2024/ + Wed, 01 Nov 2023 15:09:31 -0600 + https://HAbitsLab.github.io/news/sbm2024/ + + Dr. Alshurafa presents on SmokeMon at UbiComp/ISWC 2023 https://HAbitsLab.github.io/news/ubicomp23/ @@ -85,6 +99,13 @@ https://HAbitsLab.github.io/news/wgn9smokemon/ + + Boyang Wei's paper accepted by IEEE Journal of Biomedical and Health Informatics 2023 + https://HAbitsLab.github.io/news/jbhi_boyang/ + Sat, 04 Feb 2023 15:09:31 -0600 + https://HAbitsLab.github.io/news/jbhi_boyang/ + + Experience paper accepted at Case Studies of HCI in Practice (CHI23) https://HAbitsLab.github.io/news/experience/ diff --git a/profiles/boyang/index.html b/profiles/boyang/index.html index b518865a..ff29d1f8 100644 --- a/profiles/boyang/index.html +++ b/profiles/boyang/index.html @@ -135,6 +135,11 @@

Boyang Wei

Education

M.S. Analytics, Georgetown University

B.S. Biochemistry, University of Washington

+

Research

+

My line of research focuses on building resource-constrained ML models for behavioral and physical activities recognition from wearables.

+

I have a mixed background of Biochemistry, Computer Science and Analytics. My research interests range from machine learning, data analytics to mHealth and passive sensing.

+

Website

+

https://boyangw.com/

diff --git a/profiles/index.xml b/profiles/index.xml index 54d0eee3..7454124a 100644 --- a/profiles/index.xml +++ b/profiles/index.xml @@ -20,7 +20,7 @@ https://HAbitsLab.github.io/profiles/boyang/ Thu, 30 Dec 2021 15:09:31 -0600 https://HAbitsLab.github.io/profiles/boyang/ - <h3 id="education">Education</h3> <p>M.S. Analytics, Georgetown University</p> <p>B.S. Biochemistry, University of Washington</p> + <h3 id="education">Education</h3> <p>M.S. Analytics, Georgetown University</p> <p>B.S. Biochemistry, University of Washington</p> <h3 id="research">Research</h3> <p>My line of research focuses on building resource-constrained ML models for behavioral and physical activities recognition from wearables.</p> <p>I have a mixed background of Biochemistry, Computer Science and Analytics. My research interests range from machine learning, data analytics to mHealth and passive sensing.</p> <h3 id="website">Website</h3> <p><a href="https://boyangw.com/">https://boyangw.com/</a></p> Chris Romano diff --git a/sitemap.xml b/sitemap.xml index 4b77b734..2e687d0e 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,6 +2,12 @@ + https://HAbitsLab.github.io/news/farzad_nature/ + 2024-10-14T15:09:31-06:00 + + https://HAbitsLab.github.io/news/ + 2024-10-14T15:09:31-06:00 + https://HAbitsLab.github.io/publications/nirsighted/ 2024-09-11T15:09:31-06:00 @@ -14,8 +20,8 @@ https://HAbitsLab.github.io/news/shibosensors/ 2024-06-04T12:09:31-06:00 - https://HAbitsLab.github.io/news/ - 2024-06-04T12:09:31-06:00 + https://HAbitsLab.github.io/news/sbm2024/ + 2023-11-01T15:09:31-06:00 https://HAbitsLab.github.io/news/ubicomp23/ 2023-10-10T06:09:31-06:00 @@ -70,6 +76,9 @@ https://HAbitsLab.github.io/news/wgn9smokemon/ 2023-02-14T15:09:31-06:00 + + https://HAbitsLab.github.io/news/jbhi_boyang/ + 2023-02-04T15:09:31-06:00 https://HAbitsLab.github.io/publications/smokemon/ 2023-01-13T15:09:31-06:00 @@ -295,6 +304,9 @@ https://HAbitsLab.github.io/news/news1/ 2019-09-03T15:09:31-06:00 + + https://HAbitsLab.github.io/grants/nsf-eager-/ + 2019-06-05T15:09:31-06:00 https://HAbitsLab.github.io/news/news2/ 2019-06-03T15:09:31-06:00 @@ -369,8 +381,6 @@ 2012-06-20T15:09:31-06:00 https://HAbitsLab.github.io/categories/ - - https://HAbitsLab.github.io/grants/nsf-eager-/ https://HAbitsLab.github.io/tags/ diff --git a/tags/index.html b/tags/index.html index bfd1d157..83243c8e 100644 --- a/tags/index.html +++ b/tags/index.html @@ -304,6 +304,19 @@

Recent News +
+ Oct 14
+ 2024 +
+
+ + Farzad's paper got accepted by Nature Digital Medicine 2024 + +

A machine-learned model for predicting weight loss success using weight change features early in treatment

+
+

+
Jun 4
@@ -317,6 +330,19 @@

Recent News

+
+
+ Nov 1
+ 2023 +
+
+ + 5 SBM abstracts got accepted from HABits Lab 2024 + +

Alshurafa and the HABits Lab presented their findings from Alshurafa's K25, R03, and R21 studies at the Society of Behavioral Medicine. We received Meritorious and Citation awards, as well as a Live Research Spotlight.

+
+
+
Oct 10
@@ -382,32 +408,6 @@

Recent News

-
-
- May 1
- 2023 -
-
- - HABits Lab partners with Palbud Inc. on autism wearable project - -

HABits Lab will work with Palbud to pursue SBIR Phase 2 funding

-
-
- -
-
- Apr 9
- 2023 -
-
- - Glenn Fernandes' paper on cartoonized life-vlogging accepted by ACM SIGCHI - -

Is cartoonized life-vlogging the key to increasing adoption of activity-oriented wearable camera systems?

-
-
-
Grants

- +
- SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing -

$850,000 / PI: Nabil Alshurafa / 2018 - 2023

+ EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models +

$299,471 / PI: Nabil Alshurafa / 2019 - 2022

- +
- EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models -

$299,471 / PI: Nabil Alshurafa / 2019 - 2022

+ SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing +

$850,000 / PI: Nabil Alshurafa / 2018 - 2023