The prevalent integration of the smart devices and wearable technologies into people lives that measure the daily health and activeness has been proliferated in recent years. The potential integration of biometrics, behaviors, and other health data collected by smart devices into medical repositories offers new possibilities to health experts and a powerful tool to improve our everyday life. The technical challenge to connecting this daily activity data to public health data is to build a common bridge between them that minimizes the efforts/costs of institutions to access the data, and expands their ability to connect to the individual’s daily data. This problem generalizes to researchers outside the medical field as well, as health and fitness devices are part of an Internet of Things(IoT), but challenge remains the same. Home Hero is a smart home IOT gateway offering this bridge to connect public health data to individual wellness information. 

Home  Hero  has  been  conceptualized  and  developed  by  a 
team  at  Louisiana  Tech  as  a  device  agnostic  solution  to 
consolidate the information generated by personal IOT smart 
devices.  However,  the  challenge  proposed  here  includes 
heterogeneity  of  smart  devices  and  their  protocol,  volume 
and  velocity  of  the  data,  induvial  privacy  as  well  as  the 
interoperability with public health systems. In addition, our 
objective is to further develop a model that scales to city or 
state-wide  levels  and  build  Home  Hero  with  the  ability  to 
easily  adapt  to  new  technologies  and  standard/  upcoming 
protocols such as HL7, GPID, while creating a methodology 
that preserves user privacy and security. Furthermore,  the  Home  Hero  efforts  expand  into  directly enabling  healthier  and  smarter  living  through  a  system  to inform users of the nutritional value of their food which will be  expanded  to  generating  suggestions  based  on  dietary restrictions.

The  insect  brain  is  a  very  efficient  neural  computing  system. 
It  can  process  high-dimensional  sensory  information  and  generate 
coordinated  and  adaptive  motor  commands  in  real  time, 
resulting in various complex behaviors (including locomotion, object 
manipulation,  navigation,  and  their  combination).  Simultaneously, 
it can also autonomously learn to solve complex tasks. This amazing 
control performance is achieved by using the full capacity of its neural 
dynamics, learning, memory, and plasticity as well as by interacting 
with the environment through its body (i.e., embodiment).

Inspired  by  this,  we  have  developed  brain-like  mechanisms. The mechanisms are based on a modular concept and hierarchically organized.  They  exploit  neural  dynamics,  learning,  memory,  and plasticity, as the biological brain does, to efficiently generate complex functions of embodied multi-sensorimotor robotic systems. Based on 
this development, we have addressed the way to achieve adaptive motor  control  and  autonomous  learning  principles  for  complex locomotion, object manipulation, goal-directed navigation, and their combination in the embodied systems.