This document discusses a proposed project for an upcoming H2020 call focused on developing adaptive smart working environments to support active and healthy aging. The project would aim to improve data collection methods for monitoring musculoskeletal disorders (MSDs) using miniaturized wearable and implant sensors for real-time posture and load monitoring. Both local processing for early warnings and remote cloud-based analytics of big sensor data using machine learning are proposed. Relevant technologies, stakeholders, and potential partner institutions across Estonia, France, UK are outlined to support the transdisciplinary nature of the project.

1. 1
Cognitive Electronics (COEL) Project
Muhammad Mahtab Alam ERA-Chair Holder (COEL)
Associate Prof. PhD, MIEEE, MIET, Ceng
Thomas Johann Seebeck Department of Electronic (TJS-ELIN)
School of Information Technology, Tallinn University of Technology
Tekk Tour Digital Wallonia.
06-11.2017, Mons, Belgium
This work has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 668995.
This material reflects only the author's view and the EU REA is not responsible for any use
that may be made of the information it contains.

2. Thomas Johann Seebeck Department
of Electronic (TJS-ELIN)
1.Semiconductor materials based structures and devices
 Development of diffusion welding (cold bonding) technique for realization of improved high quality metal contacts or
wafer based anisotropic hetero-polytypic interfaces (specific wide bandgap heterojunctions);
 Lab-on-Chip solutions for medical applications (e.g. Heater design, THz measurements for detection of content of
droplets, etc.).
2.Impedance Spectroscopy and its applications
 Bioimpedance application in biological environments (e.g. Medical and rescue applications like the check of the
quality of tissue surgery);
 Impedance and eddy current measurements for material parameters detection (e.g. Constractive surgery
applications, Euro-coins quality).
3.Cognitive Electronics
 Sensorics and (wireless) sensor networks, 5G, Internet-of-Things, Wearable Electronics and Communication, Low-
Power, etc. (e.g. rescue vests for life guards, smart city solutions, etc.).
 Head of T.J. Seebeck Department of Electronics: Prof. Toomas Rang
 Staff: ca. 45, PhD students: ca. 35
 Budget: ca. 2.8 MEUR yearly

3. • Key Objectives:
• To improve the research productivity of 13 EU member states
• Improve the impact and quality of the research
• Problem solving approaches and applied research
• To build new collaborations
• Budget: 2.5 Million Euros (Project ends 31 November 2019)
• Research and Development Focus:
• Internet of Things - Wireless Sensor Networks
• Communication Technologies
• Wearable Wireless Networks
• Ultra Low Power/Energy Neutral and Harvesting
• Smart Sensor-Signal Processing
• Wireless Communications
• LTE-A/5G – device to device communication.
• Public Safety Networks – Connectivity Issues
• Cognitive Radio – Distributed Sensing and Estimation
COEL ERA-Chair (Cognitive Electronics)
EU H2020 Widespread Program 2016-2019

4. Glimpse of Our Competencies (Mahtab and Yannick)
RESEARCH DOMAIN TOPICS COVERED STANDARDS PLATFORMS COLLABORATION
S
WIRELESS BODY AREA
NETWORKS/ HEALTH
MONITORING/PUBLIC SAFETY
NETWORKS
Optimized Joint PHY-MAC system design: (specifications).
MAC Layer: (Scheduled Access, CSMA/CA). Throughput-Channel
Aware MAC.
Coexistence: Time-shared, freq. hopping – Only non-collaborative.
Realistic Performance Evaluation: MAC, Coexistence, Channel
Models (Biomechanical deterministic and dynamic models)
IEEE
802.15.6
(Strong
Experience)
Shimmer Sensor
SDKs
Network
Simulator
(WSNET – Cross
layer design)
CEA-LETI
(Grenoble, France)
INTERNET OF THINGS -
WIRELESS SENSOR NETWORKS
Accurate Hybrid Energy Model: real-time measurements +
analytical analysis
MAC Protocol: Traffic-aware dynamic MAC (TAD-MAC). Heuristic
Modeling of TAD-MAC
IEEE
802.15.4
(Good
Experience)
MSP-430 University College
Cork (Cork, Ireland)
DIGITAL SIGNAL PROCESSING Adaptive Systems, Multi-standard transceiver designs, SDR (Multi-
rate processing techniques – Channelizers, baseband processing)
IEEE 802.11
a/b/g/n
UMTS
DSP/FPGA-based
design
GateHouse
(Aalborg, Denmark)
RESEARCH DOMAIN TOPICS COVERED STANDARDS PLATFORMS COLLABORATI
ONS
EMBEDDED SYSTEMS H/W and S/W Co-design: (Design space exploration,
allocation, partitioning, scheduling).
Metrics for Characterizing Algorithms: Execution-time, area,
power
FPGA-Technology:
Altera, Xilinx.
Quartus II, ISE
DSP Builder, System
Generator
Department of
Computer
Engineering
(TTU)
LABSTICC - France
INTERNET OF THINGS -
WIRELESS SENSOR NETWORKS
Cognitive WSN: Frequency and Power Allocation Algorithm,
Reinforcement learning
Energy Harvesting: Solar, thermal, RF
Mesh Networks: IPv6
Sensor Node Platforms Department of
Computer
Engineering
(TTU)
DIGITAL SIGNAL PROCESSING Hardware implementation of baseband signal processing SDR, ADS-B,
LTE
Aalborg
University –
Denmark

5. Project Idea for Upcoming
H2020 Call
SC1-DTH-03-2018
Adaptive smart working and living environments
supporting active and healthy ageing

6. What is Musculoskeletal disorders ?
• Musculoskeletal disorders (MSDs) are injuries
and discomforts that affect the human body’s
movement or musculoskeletal system.
• The factors contributing to MSDs include but are
not limited to:
• work postures and movements,
• repetitiveness and pace of work,
• force of movements, vibration, temperature,
• increased pressure (e.g., to increase productivity).
• Certain workplace conditions such as:
• the layout of the workstation,
• the speed of work and the weight of the objects being
handled etc.

7. MSD in EU and Worldwide
• MSDs are one of the most prevalent occupational disease
in the European Union (EU)
• Throughout Europe MSDs affect millions of workers and
cost employers billions of euros
• Based on the latest survey from the Labour Force in Great
Britain, 41% of the work illness is due to MSDs
• similarly, it is the dominant health related problems among the
working population in the EU
• Such facts and statistics are spread worldwide as reported
by the Canadian Centre for Occupational Health and Safety
in and Safe Work Australia in and so on

8. Existing Works
• Existing studies on various occupational workers includes:
• computer and office workers,
• nurses, caregivers and paramedical staffs, dentists,
• cashiers etc.
• They cover the discomforts and disorders:
• in neck/shoulder, fingers/wrist/hands, upper and lowers back pains etc.
• These studies rely on huge set of data collections across various professionals among
different countries and continents.
• However, the existing data collection methods are dominantly based on “surveys
and questionnaires” which are often not very accurate. Mostly, the estimated
percentage of particular positions and postures are obtained based on mere
guess.
“There are serious concerns regarding the reliability/accuracy/real-time
requirements of the existing data collection methods”

9. Our Motivation (1/2)
• To improve such data collection methods portable sensing
platform could provide:
• miniaturized wearable and implant sensors for real-time monitoring
• obtain accurate positions, postures and orientations,
• load measurements on different bones and so on.
• To exploit the data collections at various levels:
• First, at the local level, POCT and diagnostics with alarms/ warnings.
• The local level processing and decision process:
• exploit deployed infrared cameras to monitor and keep track of the movements
• to perform “Gait analysis” early warnings and cautions can be provided to the
workers.

10. • Another important aspects are:
• remote level monitoring, processing diagnostics and early detections
• Exploiting deployed sensors and devices to transmit to the remote cloud for long-
terms predictions and estimations.
• machine learning algorithms and techniques for big data analytics
Our Motivation (2/2)

11. Technologies
 Sensing (TUT)
 Low-power near sensor processing (TUT, CNRS-UBS)
 Data communication (TUT, CNRS-Rennes 1)
 “Cloud“ (IRT-bcom, CNRS-UBO)
 Security and privacy (IRT-bcom)
 National IT infrastructures (e.g. X-Road in Estonia)
Digital adaptive services and solutions: smart work
environments for older adults (but not only)
 Remain actively involved in professional life
 Sustain and renew their work and personal life related
skills
 Support independent active and healthy lifestyles while
taking into account reduced capabilities due to age-
related health risks and conditions
Stakeholders
 Employees (older ones, but not only)
 Employers (workplaces such as factories)
 Insurance (national health insurance + private)
 Unions
 Policy makers
Medical experts
 Ergonomics (TU, Karolinska)
 Kinesiology and Biomechanics (TU)
 Re-education (TU, Karolinska)
Social experts (LU)
 Behaviour
 Sociology
 Psychology
 Gender and culture
 Laws
 Participatory design
 TUT: Tallinn University of Technology
(Thomas Johann Seebeck Department of
Electronics)
 TU: Tartu University (Institute of Sport
Sciences and Physiotherapy)
11
AllthesepointssupporttheTrans-disiplinaryaspect(seecall)
 1 CNRS Team: Lab-STICC-UBS (Diguet) / lab-STICC-
UBO (Boukhobza) / IRISA-Rennes 1 (Sentieys, Berder)
 IRT-bcom
 LU: Loughborough University
 Others ?