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I am looking for a valid and reliable wearable sensor system for gait analysis, for both clinical and research settings, what are the good brands and companies you recommend?
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For simple gait analyses, I would recommend using the BTS G-WALK® (BTS Bioengineering S.p.A., Garbagnate Milanese, Italy) inertial sensor (https://www.btsbioengineering.com/products/g-walk-inertial-motion-system/). The BTS G-WALK® sensor delivers valid ( , , ) and reliable ( , https://dergipark.org.tr/en/pub/jetr/issue/56637/737232) spatiotemporal gait parameters (including gait speed, stride duration, stride length, stance time, swing time, single support time, or double support time), is very simple to use, and has different clinical tests integrated (including timed up and go, and six minute walking test).
If you are interested in more detailed spatiotemporal gait parameters (sample report see https://research.gaitup.com/wp-content/uploads/2020/02/Gait-Sample-Report.pdf), asymetric gait (e.g. hemiparetic gait), or investigate populations where there is only limited vertical movement in the center of mass (e.g. shuffling gait), I recommend using the Physilog® sensors of the company Gait Up (https://physilog.com), as already suggested by Luigi Borzí .
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I`m organizing a testing campaign to evaluate a set tools designed for astronauts using VR (Virtual Reality). To measure the design performance we will use two well established performance evaluation methods, but they are both qualitative (NASA TLX and mSUS). I had the idea to add a quantitative control data layer to the experiment, using biometric feedbacks from wearable sensors.
Since I`m not a physician, I`m not sure which kind of data (and so sensors) will be best suited to measure stress and focus, and which indicators to look for. On the market there is a huge range of wearable sensors (hearth monitors, skin temperature and moisture, breathing levels, ECG).
Thank you for any suggestions or researches you can share on this topic.
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You can use biomarkers (hormones) to measure stress and focus.
For instance, you can track the levels of cortisol and dopamine in saliva, sweat and/or blood before, during and after the given tasks.
There are plenty of kits on the market to easily determine quantitatively those hormones.
If you need references pls let me know so I can look up some and share with you.
Best,
Hector.
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We know cardiac diseases are among the deadliest diseases in the world and smart wearables are becoming more popular among people during their daily life. So, there is a lot of data collected from people. In addition, AI/ML models reach more accuracy when they are trained with larger datasets. It looks like it is a decent match. What are the challenges and difficulties in this regard in your opinion?
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Dear Milad Eyvazi Hesar Data science is likely to lead to major changes in cardiovascular imaging. Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of artificial intelligence (AI) is dependent on robust data; the application of appropriate computational approaches and tools; and validation of its clinical application to image segmentation, automated measurements, and eventually, automated diagnosis. AI may reduce cost and improve value at the stages of image acquisition, interpretation, and decision-making. Moreover, the precision now possible with cardiovascular imaging, combined with “big data” from the electronic health record and pathology, is likely to better characterize the disease and personalize therapy. This review summarizes recent promising applications of AI in cardiology and cardiac imaging, which potentially add value to patient care.
Also, these papers will be useful:
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about tennis stroke detection using wearable sensors data
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Hi Reza ,
if you mean this link:
the dataset consists of 27 different actions, and the number of tennis strokes are as follow:
(15) tennis right hand forehand (17) tennis serve
and for my research on 2019 , no , I did not find a dataset similar to what we generate.
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I am seeking Q1-ranked JCR journals in order to publish own datasets to make them publicly available to the community.
Datasets are about wearable sensor data from smartwatches, wristband and EEG headbands. And the topics are different: emotion recognition, activities of daily living of older adults...
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I need foreign examiners for the evaluation of PhD dissertation. My area is deep learning, wearable sensors, machine learning, sensor data analysis, AI in healthcare.
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Contact me through e-mail: abidhan@nitp.ac.in
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Remote delivery of cardiac rehabilitation services using smartphones, the internet, or wearable sensors.
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Thank you, Qamar UI Islam.
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Requirements
Can anyone recommend papers of the below time-series classification?
 
 
Background
I have tried implementing a decision-making program.
The input variables include:
(1) time-series data measured by wearable sensors (numerical data)
(2) human data such as gender, birth, blood type, etc (categorical data).
 
The (1) was collected every 10ms and (2) was obtained every session.
(A session consists of 30 minutes)
The output variables are some gestures (multiple classes).
 
 
This problem has been defined as time-series classification.
 
I've read some time-series classification articles, but most classifiers deal with numerical time-series data only. (e.g. sensor data input, multiple classes output)
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For the (1) numerical time series data, I recommend our paper entitled TSFEL: Time Series Feature Extraction Library, which was written primarily inspired by features for human activity recognition.
Our lab has some work published on the topic of combining (1) and (2). I recommend this article: https://ieeexplore.ieee.org/abstract/document/8894463).
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Public health is the art and science of promoting health, preventing diseases, and increasing health and lifespan through the organized efforts of society. It is believed that sport plays an important role in physical and mental health as it incorporates physical activity. Physical activities during sports are associated with risks for injury, such as concussions in contact sports and over-use injuries in sports requiring frequent repetition of the same movement.
Although sports injuries and their aftermath have been well-studied, less attention has been given to public health, especially using technology. The use of AI-enabled IoT devices in sports can reduce the risk of injuries and enhance the efficiency, capabilities, and fitness of athletes, spectators, coaches, and officials. The use of AI-enabled IoT devices in sports and public health has huge implications for research, businesses, and future activities of mankind. This is due to the fact that the IoT devices are required to extract an unprecedented amount of health data that can be filtered, processed, and analyzed using AI and machine learning. As a result, any system based on these technologies can obtain the benefits of collecting, processing, and analyzing highly valuable data of athletes, trainers, spectators, coaches, and officials. These technologies are beneficial for injury prevention, disease transmission, on-time diagnosis, and treatments for various diseases in an easy and cost-effective way.
The aim of this Special Issue is to identify public health concerns associated with sports using AI-enabled IoT devices and machine learning algorithms. Moreover, this Special Issue will address how the brain works during sports and analyze gait techniques and human activities and their effects on health. This Special Issue aims to motivate researchers from both academia and industry to investigate and analyze various aspects of AI-enabled IoT devices and their roles in sports and public health. Any pioneering methods and algorithms detailed in original research and review articles that offer improvements in sports and public health are welcome.
Potential topics include but are not limited to the following:
  • Internet of Medical Things in sports and public health
  • AI and IoT-assisted technology in human activity recognition during sports
  • AI-enabled IoT applications in neurodegenerative health issues
  • Analyzing cognitive abilities during sports
  • Connecting the brain with sports and public health
  • Deep learning-based processing and diagnostic analysis of biomedical sensor data
  • Gait analysis based on group wearable sensors in sports
  • Novel designs in machine learning and statistical applications in health informatics
  • Intelligent monitoring of amputee behavior analysis using wearable technology
  • Digital healthcare system for athletes using sensor-based technology
  • Applications of wireless body area networks in sports
  • Data- and model-driven intelligent and smart healthcare systems
  • Novel designs of smart health services using big data analytics
  • Novel application and evaluation study in sports and public health
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Have read and digest the material i found it really helpful to my research course
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My lab has openings for graduate research scholars. I am looking for a highly motivated students for leveraging advanced bioelectromagnetics approaches to design and develop the new generation of smarter wearable sensors that provide medically accurate data. The ideal candidate will be a recent and motivated undergraduate in Electrical Engineering or Biomedical Engineering with strong academic records. The candidate will be expected to develop state-of-the-art wearable technologies to sense, perceive and control biological systems at the University of Utah. This work will contribute to the development of novel electromagnetic technologies to create innovative and impactful solutions. Visit srl.ece.utah.edu if interested.
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Thank you Dr. Benjamin Sanchez for sharing this information.
Best regards from Mexico,
Oscar J. Suarez
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If so, we're hiring! - come and join our team to help develop the next generation of intelligent, wearable drug delivery devices. Research opportunities now available in microsensor integration, transdermal delivery and microfludics, and system electronics/communications. Further details available from Dr Conor O'Mahony - feel free to discuss with us!
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nice project
good luck
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Hello All
I'm trying to synchronize imu sensor with the Motion Capture system. Is this possible? If so, how?
thanks for answering
Alireza
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Hi Alireza,
First, what is the type of motion capture system do you have? Most of them actually have their own synchronization box, which serve as trigger in or trigger out for other external device including IMU. However, if your IMU sensor is a standalone sensor without any base to connected, then you can try an option to synchronize it by using some movement that cause spike on IMU signal like jumping.
Hope this helps you
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I want to detect relative movement between fingers with wearable sensors. Which could be an appropriate technology for this?
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Dear Arpit,
I think yes. For a sport players hand motion, flex sensors (FLEX SENSOR BASED SENSOR SYSTEM- The classification capability of this system can be improved by utilizing a fuzzy logic data analysis algorithm) is enabled to the Hand Monitoring Module (HMM), which can measure the player's finger flexion angle to determine the corresponding grip type. As a result, the ability to determine the grip type allows the coach to train players to use the appropriate combination of grips to perform a winning badminton stroke. Using a glove-based apparatus to gather data on two-dimensional and three dimensional motions with accelerometer and flexible sensors is quite useful to detect finger motion.
References:
3. Saggio G, Riillo F, Sbernini L, Quitadamo LR. Smart Materials and Structures. 25, 13001 (2015).
Ashish
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Affective technologies are the interfaces concerning the emotional artificial intelligence branch known as affective computing (Picard, 1997). Applications such as facial emotion recognition technologies, wearables that can measure your emotional and internal states, social robots interacting with the user by extracting and perhaps generating emotions, voice assistants that can detect your emotional states through modalities such as voice pitch and frequency and so on...
Since these technologies are relatively invasive to our private sphere (feelings), I am trying to find influencing factors that might enhance user acceptance of these types of technologies in everyday life (I am measuring the effects with the TAM). Factors such as trust and privacy might be very obvious, but moderating factors such as gender and age are also very interesting. Furthermore, I need relevant literature which I can ground my work on since I am writing a literature review on this topic.
I am thankful for any kind of help!
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Affective technologies like social robots must answer appropriately according to context. For example, if the goal is build empathy (towards human acceptance), social robot must imitate the affect state of humans. In any way, affective technologies need recognize humans emotions first. In this context, we development this paper:
I hope it will be useful
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I am working on activity recognition using wearable sensor data. Actually, I am confused to correctly specify the window size for my activities. Here, I am considering a sliding window technique for my work.
The accurate window size plays a vital role in the detection of activity; it affects the features, and whenever any features get affected, it directly hinders the performance of a classifier. I am working on four activities (Ac1, Ac2, Ac3, and Ac4), which are totally different in nature. In the AC1, the average person’s time is at least12 s, the maximum being 20 s, to complete one cycle of AC1. On the other hand, AC2 and AC3 activities are not regular activities compared to AC1. User lasts for 4 to 6 seconds to complete one circle of these two activities. In the Ac4, the average person time is 10 second to complete the activity.
So, my question is what should be my window size for this kind of activities to correctly process? A reply would be greatly appreciated.
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Look the link, maybe useful.
Regards,
Shafagat
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Dear Colleagues,
For anyone that are working on all the interdisciplinary research topics involved in lifelog applications—data acquisition, semantic integration, data processing and mining, data categorization and summarization, and information retrieval data privacy and security, among others, there is an open call for manuscripts to the Applied Sciences journal by MDPI (Impact Factor 2.217).
Some keywords about the topic are:
- mobile/wearable sensors and devices remote heath monitoring
- data integration
- joint knowledge extraction
- semantic interoperability signal processing
- image processing
- data mining
- image and information retrieval sentiment analysis
- machine learning
- data privacy
- security
You are welcome to contributions to this topic. Please bring scientific contributions to this topic.
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Dear António J. R. Neves , I think the call is for special issue and not for all. Any way, I just want to bring into your kind attention that even if I have a manuscript almost ready to be submitted, the problem with such journal is that the publication costs is greatly exaggerated.
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My project is to design an antenna for wearable applications. Right now my substrate is PTFE (Teflon) but that is just plastic I think. I am looking for suggestions on the substrate. Can I use PTFE as a substrate for wearable applications or if I can have some suggestions on substrate material which is good for wearable applications.
Thank you
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In most of the cases, different fabric materials substrate for wearable applications such as Silk, Aravind Cotton, Lenin, Raw Silk and Terry-Cotton are used. For wearable textile antennas utilizing substrates with a low dielectric constant, the surface wave losses are reduced, and impedance bandwidth is expanded. In general, textiles material has a very low dielectric constant that reduces the surface wave losses and increases the impedance bandwidth of the antenna. The wearable textile substrate material dielectric properties depend on the parameters of resonance frequencies, temperature, surface roughness and moisture content.
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I am working on sport activity recognition based on wearable sensor data specifically accelerometer and gyroscope. I would appreciate your cooperation if you share such dataset.
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Hi, I need a wearable/fingertip health device to create a mobile application and measure the level of stress. I need at least 3 of these physiological factors: heart rate, respiratory rate, pulse oxygenation, blood pressure, HRV. In addition, I need a way to pass this data to my mobile application. Are there any devices that allow me to do that?
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Hi,
It is mighty difficult to measure levels of stress without recourse to bio-markers, matched by an array of self-reporting and administered questionnaires.
They are both pricey and time consuming, but thus far, the best shot at getting objective research results.
Cardio-vascular physiology can be influenced by other factors such as inborn genetic factors (e.g. frequent anomalies in the macro-anatomy of the vascular system) and acquired ones such as disease (e.g. thyroid gland-related, diabetes), smoking, diet, dynamic function of the gastro-intestinal system, etc.
If you still insist though, you can have a look at the following:
Leading in this area of research are the Japanese scientists - you can check there for additional data.
I hope that helps :)
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Hello everyone!
I am a new comer for the research of portable or wearable sensor for disease diagnose. If such sensor is integrated in smart phone, it will be promising. The current portable sensors are isolated from smart phone, but can be candidates for smart phone additives. Would you like to tell me about some companies, which already developed some portable sensors or detectors for health care.
Thank you very much!
Best Regards
Zha Li
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I want to attach a PDMS layer (holding surface mount devices and liquid metal interconnects) on cloth or clothing fabric for wearable sensor application. Please share your expertise.
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Dear md Monshi,
I have attached link of research articles on attachment of a PDMS layer on cloth or clothing fabric for wearable sensor application.
I hope I have answered your question.
With Best Wishes,
Samir G. Pandya
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Is there a software, standard or publications touching on how to keep track of where IoT devices are currently located?
Related Question:
Are there softwares, standards or publications touching on how to map them into a location and visually present them in their current location
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depending on the location and the correctness of the accuracy.
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Anyone having experience in multiple tag reading in NFC or RFID? Is there any way to emulate as single passive tag as multiple tags?
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The Passive UHF RFID multi-tag is handled by means of the EPC Gen-2 Protocol (https://www.gs1.org/sites/default/files/docs/epc/uhfc1g2_2_0_0_standard_20131101.pdf). There are a lot of tag ICs that fulfill that EPC Protocol. At the reader side, you should have either a UHF EPC based reader chip or implement it by your own using DSP or FPGA.
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Pls I need suggested papers on:
1. Electrode sensor for wearable ECG.
2. Electrode sensor circuit design and configuration
3. features and materials characterization for sensor
4. Biosensor for Wearable Sensor
5. Review Article on ECG electrode sensor design
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Follow the work of Professor Joe Wang http://joewang.ucsd.edu/
He is one of the leading researchers in the field of wearable sensors. Thanks.
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I am currently an investigator in a CRT using gamification to reduce obesity. Part of the intervention is to provide schoolchildren and one family member with an activity tracker and reward those who exceed a threshold of steps with points.
For the pilot study, we purchased two activity trackers: Omron HJ-324U and Jawbone UP Move. The data cannot be extracted in bulk.
Do you have any guidance on which trackers allow bulk data extraction or how this can be done?
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A little late to the party, but the Fitbit Zip would allow you to have a central capture "site." You can start a "Fitbit Challenge" and extract data off their website (either by hand or through an API).
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my descriptive model (task vs methods) for early development of a smart clothing design project for vital sign monitoring is available. In this project which did under supervision of an industrial designer, the model has drawn after completing the project.
(the model mostly illustrate an inspiration from Milton and Rodgers's (2013) book "research methods for product design" ; which termed an internal iteration within each phase. But have some addition for showing unknown condtions of project).
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In the field of Product Development and Project Management (where I work), descriptive models should be as the name implies: A "model" that tries to "describe" with a certain accuracy a given phenomenon.
As such, IMHO, it should be tested against:
1- The related acceptable and proved concepts in the body of knowledge
2- The practice. In this case it should be understood as "a reasonable description of the phenomenon " by a relevant group of practitioners. It could be done by interviews or by questionnaires.
and, if possible, tried out.
Claudiano
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We're working a single lead ECG patch which we would like test in home environment for a long term period. What particular ECG electrode brands would you recommend for long term use (2-3 weeks)? What is the feedback you have heard from test subjects on comfort and usability with the same electrodes?
I am currently using foam based Ambu White sensor electrodes.
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The other possibility might be to use those devices with a belt going around the chest. Electrodes are hidden in the belt itself; therefore, they give a non-standard signal. There is another problem, to wear any belt day and night might be at least annoying or it can cause some breathing difficulties.
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Wearable devices typically need a compact operating system to fit into low-power microcontrollers.
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thanks @U. Dreher for your answer.
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Exploring ways to integrate wearables and other sensors to track mobility and cognitive function for early development testing of drugs for Parkinson's Disease, Multiple Sclerosis, ALS, and Alzheimer's Disease
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Thanks Giuseppe.
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What is predicted to occur regarding the future (next 5 years) of wearable patches for healthcare?
Specifically how many of these patches will be sold to public commerce? As well, how many of these patches will incorporate 'smart' electronics vs. -only-chemistry (drug delivery) or will these patches have both? 
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There are so many coming out each day but most of them are not accurate and efficient enough to evaluate the health status both chemically and biologically as well for drug delivery aspects.
Indeed few are incorporating smart intelligent technologies for both diagnosis and therapy but they show up significance on trending methods for point of care and home based systems.
Patches will be having high rate of trade in the next 2 decades for sure. 
-Fredrick Johnson JOSEPH
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I am looking for a wearable programmable accelerometer sensor. Something like the Shimmer platform (http://www.shimmersensing.com/shop/shimmer3), but not that expensive (max. 150 EUR).
The platform should allow creating custom firmware, so I can implement some algorithms for data analysis locally (on the sensor itself).
Also a wireless communication is needed (WiFi or Bluetooth).
If you have any suggestions please let me know.
Thanks in advance.
Hristijan Gjoreski
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Hi Hristijan,
I would take a look at any of the following DIY platforms:
- BITalino
- Arduino
I hope it helps you on your wearable project.
Good luck!
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I am researching wearable/embeddable/ingestible technology for my MRP and am having trouble finding experts in the field of ingestibles. Any help in this area would be greatly appreciated.
.... thanks
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There has been advancements made in the area of harvesting the human bodies electromagnetic field to charge the batteries in prosthetic's. With this said there are many new scientific avenues that can be explored and or developed in the way of developing new types of technologies for purposeful focused treatments. This is very possible. Think out side the box.
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I am searching for a device to measure ECG and EDA. Ideally the device should be worn on the wrist or the arm. I prefer not to use chest band or similar. 
Thanks,
Davide
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You're going to have to be more specific about what you're trying to do, because I'm not sure there's a device that actually fulfills those precise specifications. I'll lay this out in points for simplicity:
* ECGs need a dipole, so a single-point arm or wrist device - if it exists at all - would be pretty weak. Wrist and finger devices typically are photoplethysmographic.
* If you are trying to measure any kind of episodic EDA response, you want a palmar (or foot) device. If EDA is going into a model of physical or metabolic activity, that's probably less important.
And the devices...
* Pulse oximeters, as stated, can indeed do HR monitoring from the finger or wrist. They are also prone to movement errors, and the different and various commercial versions will probably not give you much access to the pulse-to-pulse interval. Which you probably need. Consequently, they are not ideal for ambulatory monitoring, two-handed tasks and work fairly badly in some environments.
* Bitalino is great hardware - I just ordered one myself off their Kickstarter - and can be modified to do more or less whatever you want. However, you will need at least some basic biomedical engineering skills to make it work, and you will have to build it yourself.
* Movisens I haven't used but the hardware has a good reputation. The heart rate monitor is chest strap, from memory, which you don't want. If I was measuring ambulatory SCR, though, I would choose a device very similar to their EDA-sensor, which is a great build - electrodes around the thenar eminence, wrist mounted... good stuff.
Basically, we need a lot more information about what you want to do - time frame, participants, posture and movement, task content (if any), and most importantly the measures you want from the raw heart and skin c. data in the first place. There are probably 50 suitable and relatively low-cost devices that do some version of what you want to do here, and there are new builds every month as the technology is now very accessible.
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The monitoring of public spaces is a very sensitive issue as it entails the tracking and observing of people captured by a deployed network of video cameras. The monitoring may be dealt with at different levels of detail, depending on the type of technology employed, with regards to the dimensions of the monitored area, the topology of the sensor network, its location and the purpose of monitoring (security, crowd management, service delivery, etc.)
In particular face detection techniques joined with geotagging GPS Devices act as a distributed sensor node able to detect the identity, location, social connections, and more of any other person he encounters in public environment. In security surveillance perspective these ICT technologies are quite useful, but according to personal privacy the risk for abuse in such a system is substantial.
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Everyone has something to hide.  No one walks the streets naked, or goes to the toilet in the open street, or discusses their intimate health issues in a loud voice so that strangers around them can listen in.  If I walk down the street talking to my friend about personal matters and a stranger walks behind me listening in, most people would regard this as unwelcome.  Privacy is a universal aspect of human society.  Privacy is necessary for psychological well-being.  The desire for privacy does not indicate that someone has done something wrong and has something to hide.  Where people do not feel their privacy is sufficient, they experience a range of psychological issues.
We must recognise that different cultures have different levels of personal privacy, and that people inside any one culture have prefer different levels of privacy.  What constitutes "misuse" of personal data therefore varies from culture to culture and person to person.  It is therefore difficult to determine a single standard for all people in a pluralist society.  For some, the knowledge that unknown agencies are compiling unknown data about them is itself upsetting, irrespective of what it is used for.  It represents a possible threat, and that is sufficient to upset them.
We must also recognise that people abuse systems.  The mere existence of such pervasive tracking guarrantees it will be misused.  Criminal sanctions will not prevent such misuse.  We know NSA staff have used internet survelliance to track old girlfriends, for example.  Sanctions may deter a few, but mostly it will be used to punish people after the abuse has happened.  Criminals never committ crimes expecting to be caught.  In addition, with online services the way they are, the bigger danger is that misuse will be impossible to detect.  If I fail to get a job because my employer had illegal access to personal information about me, how would I know, or be able to prove it.
It is not automatic that such pervasive tracking would improve police efficiency.  The influx of huge amounts of additional data could just as easily confuse and overload police.  If we wanted to get real efficiency, we should all be wearing personal gps trackers all the time.  It would then be simple to determine that someone had broken into a house and robbed it.  If you don't like the idea of wearing a device which reports your location to your government all the time, then you shouldn't like the idea of pervasive survellience, because the only difference is the technology being used - wearing it vs placing it in the street.
The fact that privacy is not considered important by people is not really relevant.  This is a technically complex area, and most people simply don't know what's going on.  In addition, most fail to realise the potential consequences of misuse.
As to whether pervasive survelliance represents an improvement to the quality of life, it depends much on your definition.  For some, reduction in crime and improved chances to prevent terrorist attacks are more important than personal privacy.  However, the degree of risk reduction for the individual, whose personal chance of being a victim of crime or terrorism is statistically tiny, may be very low. However, many people value their independance from government and privacy more and are prepared to accept a slight increase in risk if it means preserving their privacy.