Top 5 Killer Apps: QS-Automotive Sensors

The Internet of Things means not just that computing devices have connectivity to the cloud but that they are connected to each other, and therefore that novel applications can be developed in this rich ecosystem. One area for development is linking quantified self wearable sensors with automotive sensors for applications including Fatigue Detection, Real-time Parking and Assistance, Anger/Stress Reduction, Keyless Authentication, and DIY Diagnostics.

The auto industry may be poised for tremendous change in the next two decades with self-driving cars, denser cities, more cars on the road, and alternative fuel sources expected. This suggests new concepts in personal transportation, including redefining 'what a car is' to shift from a 'dumb conveyance' to an interactive platform communicating in real-time with other drivers, smartcity infrastructure, driver and passenger biometric data, and other sensor/internet of things information streams.

 Smart Pod Conveyance of the Future?

 (Image: M. Ghezel)

Top 5 Killer Apps 

1. Fatigue Detection

• Fatigue is implicated in 20% of accidents. Early warning signs are a slower driver heart rate and breathing rate, and posture slump. These could be detected through wearable sensors or auto-based sensors, and an intervention provided (verbal alert, seat vibration, music, or puff of air). 

2. Real-time Parking and Assistance

• Up to 75% of city center congestion may be caused by drivers looking for parking. Parking garage data could be connected to on-board navigation systems to show and guide drivers to available spots, and further reserve and pre-pay for spots where a user presents a QR code on a smartwatch or smartphone to a smart parking gate like from SureSpot to obtain the parking ticket [and directions to the spot]. 
• A related idea is real-time automatic road-side assistance, where automotive sensors would assess crash impact and predict damage. Then if appropriate the vehicle could alert local trauma centers (tier 1-5) and first responders. If the accident is less serious, if the driver has permissioned such a service, an app could automatically request local vendor service quotes.

3. Anger/Stress Reduction

• Anger reduction is the most obvious area for improvement where most simply the driver’s mental state could be read from sensors and interventions provided such as breathing exercises, music, and question-based (re-focusing) intervention. 
• Smart steering wheels with heart sensors could be used to detect heart attacks. Medical emergencies are implicated in 1% of accidents, and this number is growing with active adults driving longer, and commute distances lengthening. 
• Wearable or auto-based sensors could provide a daily health check that is completely transparent to the driver measuring heart rate, respiration, blood pressure, skin conductance, and glucose levels, and sent through the cloud to the driver’s personal EMR or QS data portal. 
• Addressing stress as a complex adaptive system, multiple data streams could be integrated into a ‘leave on time’ app. A key stressor in distracted driving is being late. An individual’s online calendar could be connected with real-time traffic data so smarthome or smartwatch alerts communicate to leave earlier for an appointment and confirm if this happens, and measure drive-time stress. Financial incentives could be offered for both health and auto insurance discounts for reduced stress and smart driving.

4. Keyless Authentication

• Keyless authentication, could facilitate one-time or short-term access, for example for automated car rental, assuming anti-theft concerns are allayed. Vehicle authentication and access could be via Bluetooth, QR code, blockchain technologies, and/or smartwatch fingerprint readers for an added layer of validation.

5. DIY Diagnostics

• DIY diagnostics accessed with tools like the CarChip could be an important app. Just like DIYscience and DIY health, on-board diagnostic data could be collected and linked to user-friendly consumer apps for pro-active notification and preventive maintenance. Asynchronous reminders (later while the driver is relaxing at home) could consist of the vehicle tweeting the driver more granular detail about its condition and potential maintenance, including the projected cost per different future time points if the maintenance is delayed.

More Details and References to Statistical Citations: Sensor Ubiquity: Blockchain Tech and Automotive-Quantified Self Integrated Sensor Applications developed for Toyota's Collaborative Safety Research Center.

Intracortical Recording Devices

A key future use of neural electrode technology envisioned for nanomedicine and cognitive enhancement is intracortical recording devices that would capture the output signals of multiple neurons that are related to a given activity, for example signals associated with movement, or the intent of movement. Intracortical recording devices will require the next-generation of more robust and sophisticated neural interfaces combined with advanced signal processing, and algorithms to properly translate spontaneous neural action potentials into command signals [1]. Capturing, recording, and outputting neural signals would be a precursor to intervention and augmentation.

Toward the next-generation functionality necessary for intracortical recording devices, using organic rather than inorganic transistors, Bink et al. demonstrated flexible organic thin film transistors with sufficient performance for neural signal recording that can be directly interfaced with neural electrode arrays [2].

Since important brain network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices, high-density active electrode arrays may be one way to provide a higher-resolution interface with the brain to access and influence this network activity. Integrating flexible electronic devices directly at the neural interface might possibly enable thousands of multiplexed electrodes to be connected with far fewer wires. Active electrode arrays have been demonstrated using traditional inorganic silicon transistors, but may not be cost-effective for scaling to large array sizes (8 × 8 cm).

Also, toward neural signal recording, Keefer et al. developed carbon nanotube coated electrodes, which increased the functional resolution, and thus the localized selectivity and potential influence of implanted neural electrodes. The team electrochemically populated conventional stainless steel and tungsten electrodes with carbon nanotubes which amplified both the recording of neural signals and the electronic stimulation of neurons (in vitro, and in rat and monkey models). The clinical electrical excitation of neuronal circuitry could be of significant benefit for epilepsy, Parkinson’s disease, persistent pain, hearing deficits, and depression. The team thus demonstrated an important advance for brain-machine communication: increasing the quality of electrode-neuronal interfaces by lowering the impedance and elevating the charge transfer of electrodes [3].

Full Article: Nanomedical Cognitive Enhancement

References:
[1] Donoghue, J.P., Connecting cortex to machines: Recent advances in brain interfaces. Nat. Neurosci. 5 (Suppl), 1085–1088, 2002.
[2] Bink, H., Lai, Y., Saudari, S.R., Helfer, B., Viventi, J., Van der Spiegel, J., Litt, B., and Kagan, C., Flexible organic electronics for use in neural sensing. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 5400–5403, 2011.
[3] Keefer, E.W., Botterman, B.R., Romero, M.I., Rossi, A.F., and Gross, G.W., Carbon nanotube coating improves neuronal recordings. Nat. Nanotechnol. 3(7), 434–439, 2008.

Quantified Self Fourth Person Perspective and Self 2.0

Quantified self trackers¹ are having an increasingly intimate relationship with technology and data flow in mediating their experience of reality. Technology effectively opens up a new perspective (as vaunted by Nietzsche), a fourth person perspective – a new and objective view of the self, possibly on the road to creating the overself (self 2.0). An important and radical aspect of quantified self (QS) activity is its inherent linkage of the former binary of quantified and qualified in three important ways:

1) The QS Act Itself 
The very act of QS’ing fundamentally includes both the collection of objective metrics data and the subjective experience of the impact of these data

2) QS’ing the Qualitative 
QS methods are now being applied to the tracking of (formerly objectively inaccessible) qualitative phenomena such as mood (e.g.; tracking qualitative word descriptors or mapping subjective experience onto quantitative scales)

3) Quant-Qual are part of a Larger Phenomenon 
To understand QS’ing is to see that it is part of a larger more complex process in which the quantified data collection and the qualitative experience of the data are nodes in feedback loops for behavior change. Data, information, understanding, and action are constituent parts of the looping process

¹Quantified self activity: the self-tracking of any kind of biological, physical, behavioral, or environmental information, often with a proactive stance towards action

Quantified-self Experimentation Platforms

In the burgeoning Quantified Self (QS) movement, one recent trend is the emergence of tools explicitly for the conduct of QS experiments, either individually or in groups. These tools offer the rapid design and launch of experiments, and usually some degree of automated operation, data analysis, and recruitment.

On the mobile platform, there is PACO, the Personal Analytics Companion for the design and operation of private or shared personal science experiments. Another tool is studycure, an online platform that allows users to create and run interactive experiments using simple if/then logic to help users design experiments.

Community self-experimentation networks also exist, such the health collaboration community Genomera where professional researchers, non-profit groups, and individuals run studies examining a range of issues such as sleep quality, vitamin deficiency, microbiomic profiling, empathy-building, and how the memory works.