Conformal Electronics for Human Electrophysiological Monitoring
Lead Research Organisation:
University of Manchester
Department Name: Electrical and Electronic Engineering
Abstract
Conformal electronics are envisioned as the next generation of wearable sensor node. They attach directly to the skin as a 'temporary tattoo' and can provide very high quality and very long term measures of body parameters. This project is creating Silver and Graphene printed sensors for flexible and conformal substrates. These may find use as temperature sensors for monitoring Diabetic foot ulcers, as long term heart monitors in embedded in T-shirts with improved wash-ability compared to previous approaches, or in non-invasive brain monitors. The key technical challenge is in the sensor node design and maintaining sensitivity, noise performance and longevity while minimising the amount of printed material to speed up and personalise the manufacturing.
Organisations
Publications
Karim N
(2017)
All inkjet-printed graphene-based conductive patterns for wearable e-textile applications
in Journal of Materials Chemistry C
Krachunov S
(2017)
Energy efficient heart rate sensing using a painted electrode ECG wearable
Beach C
(2018)
Performance of graphene ECG electrodes under varying conditions.
in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Beach C
(2018)
Optimizing Energy Harvesting for Foot Based Wearable Sensors.
in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Beach C
(2018)
An Ultra Low Power Personalizable Wrist Worn ECG Monitor Integrated With IoT Infrastructure
in IEEE Access
Velcescu A
(2019)
Flexible 3D-Printed EEG Electrodes.
in Sensors (Basel, Switzerland)
Beach C
(2019)
A Graphene-Based Sleep Mask for Comfortable Wearable Eye Tracking.
in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Beach C
(2020)
Inertial Kinetic Energy Harvesters for Wearables: The Benefits of Energy Harvesting at the Foot
in IEEE Access
Beach C
(2021)
Monitoring of Dynamic Plantar Foot Temperatures in Diabetes with Personalised 3D-Printed Wearables.
in Sensors (Basel, Switzerland)
Beach C
(2021)
Impact of shift working on the potential for self-powering via kinetic energy harvesting in wearable devices.
in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509565/1 | 01/10/2016 | 30/09/2021 | |||
| 1830714 | Studentship | EP/N509565/1 | 01/10/2016 | 31/03/2020 | Christopher Beach |
| Description | Part 1, Diabetic Foot Ulcers: Designed device to measure temperature at the sole of the foot in participants with diabetes, that can also be personalised to match the anatomical measurements of their feet. We found that the temperature rise time is faster in participants with diabetes, which may be useful as a biomarker for the development of a diabetic foot ulcer. Part 2, Energy Harvesting at the Foot: Estimations of the energy that can be harvested with a kinetic energy harvester across the body were found. The amount of energy that can be harvested from the foot is around 26 times the amount that can be harvested from the wrist. It was also found that the energy harvesting at the wrist is more sensitive to changes in walking speed compared to the foot. Previous work has used the ankle as a proxy location for estimating energy harvesting at the foot, but this has been found to be inappropriate as both more energy can be harvested from the foot, and the foot is less sensitive to changes in cadence affecting energy harvesting output. Part 3, Large-Scale Energy Harvesting: We used the UK Biobank activity data to identify levels of energy that can be harvested from a population size datasets and stratify this by age, sex and presence of diseases. We found that the time of day when the most energy is available from energy harvesting is around 10am. This work also identified that the presence of a disease such as diabetes as well as age has a significant impact on energy harvester output. Part 4, Flexible Electronics: A new wrist worn wearable with over a month of battery life for heart rate monitoring through the ECG was developed. This device is also able to extract some heart rate variability features, and can be customised to the user. New methods for manufacturing printed graphene were investigated for their ability to record ECG, EEG and EOG waveforms in a wearable/textile form factor. It was found that the graphene-based materials were generally suitable for this task, although they have lower signal-to-noise ratios compared to conventional materials (ie. Ag/AgCl), they offer better comfort for the user. |
| Exploitation Route | Part 1, Diabetic Foot Ulcers: Further research into the rate of temperature change at the foot to identify if this is a precursor to the development of a diabetic foot ulcers as this has not been previously investigated. Indications of the recommended minimum sample size for future research into measuring diabetic foot ulcers. Part 2, Energy Harvesting at the Foot: Wearable devices can be made with energy harvesters at the foot to take advantage of the extra energy available at this location compared to conventional device locations at the wrist. The ankle should not be assumed to be a suitable proxy for the foot when estimating the output in energy harvesting from the foot Part 3, Large-Scale Energy Harvesting: This work gives indicative levels of a wearable energy harvester output when worn by different population groups in the UK. The outputs from this work allow wearable designers to identify suitable power budgets to design their devices towards for specific groups to ensure their devices can be powered by energy harvesting. Part 4, Flexible Electronics: Usage of graphene-based materials in consumer devices to allow 'out of the hospital monitoring' of various health conditions by recording the ECG, EEG and EOG from these new materials. |
| Sectors | Digital/Communication/Information Technologies (including Software),Electronics,Healthcare,Manufacturing, including Industrial Biotechology |
| Description | EPSRC eFutures grant to attend IEEE UKCAS Workshop |
| Amount | £50 (GBP) |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 12/2019 |
| End | 12/2019 |
| Description | IET Travel Award |
| Amount | £500 (GBP) |
| Organisation | Institution of Engineering and Technology |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 06/2018 |
| End | 07/2018 |
| Description | University of Manchester Presidents Doctoral Scholar |
| Amount | £3,500 (GBP) |
| Organisation | University of Manchester |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 09/2016 |
| End | 03/2020 |
| Title | Accelerometer Data for Energy Harvesting During Walking Estimation |
| Description | Accelerometer data supporting Inertial Kinetic Energy Harvesters for Wearables: The Benefits of Harvesting at the Foot, this dataset contains accelerometer data from participants walking on a treadmill at a variety of speeds with sensors on the wrist, hip, ankle and foot. If using this data, please cite: C. Beach, A. J. Casson, "Inertial Kinetic Energy Harvesters for Wearables: The Benefits of Harvesting at the Foot," IEEE Access, 2020 (doi: doi.org/10.1109/ACCESS.2020.3037952) Analysis code for this paper is available at https://github.com/CASSON-LAB/kinetic_energy_harvesting This repository includes both the raw data collected by the Axivity AX3 sensors (the raw CWA files and the resampled data in CSV format) and versions of the data that has been trimmed into multiple records corresponding to each speed of the treadmill (in pickled format). Note there is no participant P1. Accessing the raw data: The folders P2 - P13 contain the untrimmed data from the sensors. Sample rate: 100 Hz, units: g Participants were instructed to walk on a treadmill (LifeSpan TR1200i) as close as possible to how they would normally walk, while the speed of the treadmill was controlled by the experimenter. The treadmill was started at 2.4 km/h and the speed increased every 60 s by 0.1 km/h until the treadmill reached 4.3 km/h. Prior to recording each sensor went under a synchronisation procedure where all the sensors were flipped on their z-axis, causing a transition from -1g to +1g. The times of this synchronisation and the time for starting the treadmill is detailed in metadata.xlsx Files ending in .cwa are in Continuous Wave Accelerometer format (a binary format) which can be processed with OmGUI software from Axivity. Files ending in .csv are these files are processed cwa files in a text format, files ending in .resampled.csv have been resampled to 100 Hz using OmGUI. These resampled files account for the fact that the AX3 sensors sample at close to 100 Hz with significant sampling jitter by resampling the data to make the sampling rate exactly 100 Hz. It is recommended to work with the sampled files. 21629: Left wrist 21704: Right wrist 31447: Left hip 32610: Right hip 32784: Left ankle 32798: Right ankle 32816: Left foot 32973: Right foot Accessing the trimmed data: The trimmed data can be accessed by downloading the .pkl files, which are suitable to be imported directly into Python. Each of these can be imported by running the following commands in Python: import pickle import numpy as np pkl_file = open('P2.pkl', 'rb') P2 = pickle.load(pkl_file) The files cached_indexes.h5, cached_data.h5 and cadence_list.csv are required for use with the analysis code used in the paper and available at https://github.com/CASSON-LAB/kinetic_energy_harvesting |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Details accelerometer signals from four locations on the body (the wrist, hip, ankle and foot) on both sides of the body. |
| URL | https://data.mendeley.com/datasets/5rrxw7y5bj/1 |
| Title | Plantar foot temperature during sitting and standing in participants with diabetes and controls |
| Description | Continuous temperature data collected from the plantar (sole) of the foot of 13 participants with diabetes and 12 healthy control participants during sitting and standing. Collected with our custom personalised temperature sensing insoles (see paper for description). If using this data please cite: Beach, C.; Cooper, G.; Weightman, A.; Hodson-Tole, E.F.; Reeves, N.D.; Casson, A.J. Monitoring of Dynamic Plantar Foot Temperatures in Diabetes with Personalised 3D-Printed Wearables. Sensors 2021, 21, 1717. https://doi.org/10.3390/s21051717 The data presented here has been converted into °C and filtered using a low-pass filter with cut-off frequency of 0.02 Hz. Temperature data is provided in csv format and the participant metadata is in xlsx format. In addition data is provided in Python serialized (pickle) format to allow easy importing to Python (see below for steps to do this). Use of the Anaconda Distribution is recommended. In the metadata, Testing Date 1 refers to date where pressure mat data was collected to inform the personalised insole design. Testing Date 2 refers to the date where the temperature data uploaded here was collected. To import this data into Python use the following commands: import numpy as np import pandas as pd import pickle as pkl with open('participants.pkl', 'rb') as f: participants= pkl.load(f) # This line needs to be indented, however Mendeley is removing this formatting 'participants' is then a dictionary containing dictionaries for each participants. Within each of these dictionaries are four Pandas DataFrame's containing the temperature data for each foot, for sitting and standing. For example, if you wanted to access the DataFrame for the left foot of participant H1 during standing you would type: participants['H1']['sitting_L'] Within each DataFrame each column corresponds to: Time: The timestamp of each datapoint in HH:MM:SS as recorded by the iPhone app Ch0: Temperature data from the Hallux Ch1: Temperature data from the 1st Metatarsal Head Ch2: Temperature data from the 5th Metatarsal Head Ch3: Temperature data from the Calcaneus So if you wanted just the temperature data from the left 1st Metatarsal Head of participant H1 during sitting you would type: participants['H1']['sitting_L']['Ch1'] |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Continuous temperature data recorded at the foot in participants with diabetes and healthy controls. |
| URL | https://data.mendeley.com/datasets/ppwxdgbbx4/1 |
| Title | SPHERE wrist ECG Data |
| Description | Data collected using the newly developed SPHERE ECG wrist device described in our published paper in IEEE Access (https://doi.org/10.1109/ACCESS.2018.2864675) |
| Type Of Material | Database/Collection of data |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Impact | Data shows how ECG data can be collected from the wrist with a very low power budget. |
| URL | https://data.mendeley.com/datasets/tzfmydyzch/1 |
| Title | BiobankActivityCSF |
| Description | Software to process the accelerometer CWA files from the UK Biobank activity monitoring study in Python, and optionally on high performance computing infrastructure. This code is an adaption of biobankAccelerometerAnalysis from Aiden Doherty/University of Oxford. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | Software allows processing of UK Biobank activity data. Has enabled high temporal resolution estimates of free-living energy harvesting (paper currently in process of being written). |
| Title | Energy Harvesting MATLAB Toolbox |
| Description | A MATLAB GUI tool for modelling an inertial energy harvester and storage device. It allows the user to load a dataset of accelerometer data and to modify the harvester parameters to identify the power waveforms from this harvester. |
| Type Of Technology | Software |
| Year Produced | 2018 |
| Open Source License? | Yes |
| Impact | The GUI and dataset help designers specify the energy budgets of their wearable devices. |
| Description | IET Faraday Challenge |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Schools |
| Results and Impact | Assisted in the running of the IET Faraday Challenge Activity at the University of Manchester. Focused on widening participation schools and engaging them with engineering. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://faraday-secondary.theiet.org/faraday-challenge-days/ |
| Description | Japan IoT Workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Presentation at the the UK embassy in Tokyo Japan in the Joint UK/Japan IoT workshop to academics and experts from industry as well as representatives from embassies in Japan and the UK. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Outreach Workshop at St. Annes Primary School |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Schools |
| Results and Impact | Delivered an outreach workshop to a Primary school class at St. Annes Primary School in Sale, Manchester. Talked about my research and delivered an Engineers Without Borders UK workshop on 'Power for Everyone Everywhere' |
| Year(s) Of Engagement Activity | 2019 |
| Description | Presentation to EEG research group, Fudan University, China. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Discussions of potential collaboration opportunities |
| Year(s) Of Engagement Activity | 2018 |
| Description | Presentation to IEC working group 119 (printed electronics) and 124 (wearable electronics), Manchester, UK. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Presentation on printed electronics at the University of Manchester for the IEC standards committee. Input into shaping future standards for wearable devices, with a follow on conference to be held in Manchester in 2020 as a result of this. |
| Year(s) Of Engagement Activity | 2018 |
| Description | ScienceX 2019 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Science outreach events on "paper circuits" showing how simple electronics can be made flexible. |
| Year(s) Of Engagement Activity | 2019 |
| Description | Singapore A-Level Outreach Presentation |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Schools |
| Results and Impact | Members of research group including myself gave presentations about our research to prospective students to the University of Manchester visiting from Singapore. |
| Year(s) Of Engagement Activity | 2017 |
| Description | Stand on Brain-Computer Interfacing at the Intu Trafford Centre as part of the "ScienceX" festival. An estimated 3000 visitors. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Hands on brain-computer interfacing demo. |
| Year(s) Of Engagement Activity | 2018 |