Dr Adnan Nadeem
People grow through experience if they meet life honestly and courageously. This is how character is built.
Time passes faster and faster, but with every project I always want to find the next challenge and the next challenge is just as exciting as the previous one
Dr Michael Howarth (University of Surrey, UK), Prof George Pavlou (UCL, UK), Dr Ning Wang (University of Surrey, UK), Dr Qammar Abbasi (James Watt School of Engineering, University of Glasgow, UK), Dr Nadeem Mehmood (University of Karachi , Pakistan), Dr Amjad Pervaiz (SARC, Pakistan), Dr Kamran Arshad (University of Greenwich, UK), Dr. Soamsiri Chantaraskul (Mongkut's University of Technology , Thailand ), Dr Kamran Ahsan (FUUAST, Pakistan), Dr Kashif Rizwan (FUUAST, Pakistan)
Department of Lost & Found, Masjid e Nabvi ( Madinah Pollice,KSA), Department of Traffic (KSA), Ministry of Interior, KSA, University of Karachi, Edhi Foundation, Darul Sukun (Elderly Home), Pakistan, PARC, Agriculture Research Sindh Pakistan, Artificial Blockchain Automations, UK.
Creativity is putting your imagination to work, and it's produced the most extraordinary results in human culture
The whole purpose of education is to turn mirrors into windows
I hope I raised more questions than I have given answers
Do the thing you're good at. Not many people are lucky enough to be so good at something
Research is creating new knowledge
I have completed multiple research projects, including, Adaptive Intrusion Detection and Response Mechanism for Mobile Ad Hoc Applications, remote vital sign monitoring & assistance of patients using BASN, Provision of Security, reliability and QoS in BASN applied to healthcare applications. He has proposed various solutions for network layer security attacks by IDS, IPS, QoS and reliability issues of Mobile Ad Hoc and sensors Networks. Currently he is focusing on investigating & providing solutions for real community problems through modeling and assistive technology. He also working on project on emerging technologies such as Blockchain challenges and applications. He has published over 55 peer review papers in international journal and conferences including a patent submission. Total Impact Factor of his publications is 33.30. He has also presented his research and tutorials in several international conference and seminars including IEEE WCNC, IEEE ICUMT, IEEE INMIC etc. Current Impact Factor of my publications is 33.30 (Research Gate: http://www.researchgate.net/profile/Adnan_Nadeem2). My research work has been cited more than 920 times see google scholar: (https://scholar.google.com.pk/citations?user=9fXG_OkAAAAJ&hl=en ). APatent accepted and sealed by the Government of Pakistan, in 2019.
|1||Dr Amir Mehmood||Self-organized applications of Body Area Sensor in healthcare||Completed in 2020|
|2||Dr Azhar Hussain||Assisting Disabled & Elder Person by Exploiting Mobile Technology & Ubiquitous Computing||Completed in 2020|
|3||Dr Sarwat Iqbal||Context Aware Assistive Technology for Elderly using Mobile Technology||Completed in 2020|
|4||Mr Saeed Azfar||Monitoring & Detection of Cotton Crop Insects and related Disease using WSN & IoT||In Progress|
|5||George Marinos||enial of Service Attack detection in DSR routing protocol in MANETs||MSc (University of Surrey, United Kingdom)|
|6||Yu Li||Analysis of Routing protocols for Delay Tolerant Networks||MSc (University of Surrey, United Kingdom)|
|7||Abdul Salam||A Class based Quality of Services Model for Body Area Sensor Networks||MS (FUUAST, Pakistan)|
|8||Obaid Khan||RPRP: Routing Protocol to Ensure the Reliability in Health Care System||MS (FUUAST, Pakistan)|
|9||Anis Ahmed||Sybil Attack Detection in Mobile Ad Hoc Networks (In complete)||MS (FUUAST, Pakistan)|
|10||M.Amir Nisar||Performance Analysis of Mobility models of MANETs and VANETs||MS (FUUAST, Pakistan)|
Life is 10% what happens to you and 90% how you react to it. Read more at:
SMotionDataSet: Published by Harvard Dataverse (Download -> Dataset:)
Abstract: The dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable shimmer sensors.
|Data Set Characteristics:||Multivariate, Time-Series||Number of Instances:||114||Area:||Computer|
|Attribute Characteristics:||Real||Number of Attributes:||14||Date updated||2019-04-20|
Dar ul Sukoon, Elderly Care Home, Karachi
EDHI Foundation Old age home, Karachi
Department of Special Education, University of Karachi
Department of Computer Science ,Federal Urdu University of science Art and Technology
Email to whom correspondence should be addressed: firstname.lastname@example.org
Data Set Information:
The dataset comprises body motion recordings for several volunteers of diverse profile while performing certain physical activities. Sensors placed on the subject's waist is used to measure the motion experienced by diverse body parts, namely, acceleration and rate of turn. Data is divided into five age and weight groups categories.
|S. No.||Age Groups||Male||Female||Total|
|1||41 – 50 yrs.||3||3||6|
|2||51 – 60 yrs.||33||30||63|
|3||61 – 70 yrs.||13||6||19|
|4||71 – 80 yrs.||13||3||16|
See the db file with groups
Sensor devices: 1
The collected dataset comprises body motion and vital signs recordings for several volunteers of diverse profile while performing 3physical activities (Table 1). Shimmer3 wearable sensors were used for the recordings. The sensors were respectively placed on the subject's waist attached by using elastic straps (as shown in the figure in attachment). The use of multiple sensors permits us to measure the motion experienced by diverse body parts, namely, the acceleration, the rate of turn, thus better capturing the body dynamics. All sensing modalities are recorded at a sampling rate of 50 Hz(normal range), which is considered sufficient for capturing human activity. few session was recorded using a video camera. The activities were collected in an out-of-lab environment with no constraints on the way these must be executed, with the exception that the subject should try their best when executing them.
The activity set is listed in the following:
L1: Standing still (5sec)
L4: Walking (1 min)
L11: Stand to Sitting (3 steps) (time varies)
NOTE: In brackets are the number of repetitions (Nx) or the duration of the exercises (min).
A complete and illustrated description (including table of activities, sensor setup, etc.) of the dataset is provided in the papers presented in the presentation section.
The data collected for each subject is stored in a different log file: 'shimmer XXX.xls'(XXX will be number between 001 to 999). Each file contains the samples (by rows) recorded for all sensors (by columns). The labels used to identify the activities are similar to the abovementioned (e.g., the label for walking is '4').
The meaning of each column is detailed next:
Column 1: Time stamp raw
Column 2: Time stamp in millisecond
Column 3: acceleration raw (X axis)
Column 4: Acceleration cal (X axis)
Column 5: acceleration raw (Y axis)
Column 6: Acceleration cal (Y axis)
Column 7: : acceleration raw (Z axis)
Column 8: Acceleration cal (Z axis)
Column 9: gyro raw (X axis)
Column 10: gyro cal (X axis)
Column 11: gyro raw (Y axis)
Column 12: gyro cal (Y axis)
Column 13: gyro raw (Z axis)
Column 14: gyro cal (Z axis)
*Units: Acceleration (m/s^2), gyroscope (deg/s).
Use of this dataset in publications must be acknowledged by referencing the following :
A.Nadeem, K. Rizwan and N.Mehmood, "SMotion dataset', developed under the project of fall detection system for elderly, available at http://adnan-nadeem.com/data-repository/.
We would appreciate if you send us an email (adnan.nadeem at fuuast dot edu dot pk) to inform us of any publication using this dataset.