Methods of operating wireless parameter-sensing nodes and remote host
Discretely-mobile parameter-sensing nodes, such as mobile phones, may be connected to a remote host. Each of the nodes is capable of sensing parametric values that are internal, and ambient to the node. The values may relate to atypical movement of a group of users, such as warfighters, that are associated with the nodes. Upon analysis of the atypical movement, the status (e.g., health) of a user associated with a node involved in such movement can be ascertained.
1. A method of operating a remote host in a network of discretely mobile wireless parameter-sensing nodes, each node including sensors for sampling values of parameters that are at least one of internal to and ambient to the node, respectively, and circuitry to collect data representing the sampled values, and send the same to a remote host,
the method comprising:
receiving the collected data from the discretely-mobile wireless parameter-sensing nodes at the remote host, where at least some of the collected data comprises data regarding accelerations undergone by at least some of the nodes;
organizing the collected data into groups according to proximity of each of the discretely-mobile wireless parameter-sensing nodes to each other;
determining group-specific ranges regarded as typical by comparing the collected data corresponding to the groups to reference thresholds;
determining, within the groups, whether there are one or more nodes exhibiting atypical collected data by comparing the collected data to the determined typical ranges; and
adaptively instructing, within the groups at least one of the nodes to execute one or more tasks that are responsive to the determination of atypical collected data.
2. The method of claim 1 , wherein:
the nodes are borne by users, respectively; and
wherein the data regarding accelerations undergone by the nodes represent acceleration spikes, respectively.
3. The method of claim 2 , wherein the tasks further comprise tasks that are responsive to one or more common events, the tasks comprising one or more of:
requesting a user of a given node effected by the one or more common events to interact with a man-machine interface on the given node;
streaming a video signal from a given one of the nodes effected by the one or more common events to the remote host; and
instructing a user of a node not effected by the one or more common events albeit proximate to the node effected by the one or more common events to inspect the health of a user of the node effected by the one or more common events.
4. The method of claim 3 , wherein adaptively instructing at least one of the nodes to execute the one or more tasks that are responsive to the determination of the atypical collected data further comprises:
instructing at least one of the first and second tasks;
receiving the resultant data, respectively; and
determining, from the resultant data, whether to instruct the third task.
5. The method of claim 2 , further comprising:
comparing a set of reference thresholds representing escalating human-physiology danger levels against each of the acceleration spikes, respectively, to determine a corresponding danger level; and
wherein adaptively instructing at least one of the nodes to execute the one or more tasks that are responsive to the determination of the atypical collected data further comprises determining, for each node, one or more tasks that are appropriate to the corresponding danger level.
6. A method of operating a remote host in a network of one or more discretely-mobile wireless parameter-sensing nodes, each node being disposable in a holster worn by a user, and each node comprising sensors for sampling values of parameters that are at least one of internal to and ambient to the node, and circuitry to collect data representing the sampled values, and send the same to a remote host, the method comprising:
receiving portions of the collected data from a given one of the one or more discretely-mobile wireless parameter-sensing nodes including a holstering status and motion-data representing sampled values of motion parameters for the given one or more discretely-mobile wireless parameter-sensing nodes;
inspecting the holstering status to identify if the given one or more discretely-mobile wireless parameter-sensing nodes is holstered on a user;
analyzing the motion-data of the one or more discretely-mobile wireless parameter-sensing nodes identified as being holstered according to human-physiology constraints; and
adaptively instructing the one or more discretely-mobile wireless parameter-sensing nodes to execute one or more tasks that are responsive to results of the analysis.
7. The method of claim 6 , wherein:
the motion-data includes acceleration data; and
wherein analyzing the motion-data of the one or more discretely-mobile wireless parameter-sensing nodes identified as being holstered according to human-physiology constraints further comprises:
comparing a set of reference thresholds representing escalating human-physiology danger levels against the acceleration data, respectively, to determine a corresponding danger level; and
wherein adaptively instructing one or more discretely-mobile wireless parameter-sensing nodes to execute one or more tasks that are responsive to results of the analysis further comprises:
determining one or more tasks that are appropriate to the corresponding danger level.
8. The method of claim 6 , wherein:
the motion-data includes acceleration data; and
wherein analyzing the motion-data of the one or more discretely-mobile wireless parameter-sensing nodes identified as being holstered according to human-physiology constraints further comprises:
recognizing that one of the one or more discretely-mobile wireless parameter-sensing nodes underwent motion substantially immediately after an acceleration-event so as to arrive at a first position, respectively;
recognizing that:
if the one discretely-mobile wireless parameter-sensing node that underwent motion substantially immediately after an acceleration-event has undergone substantially no motion relative to the first position after elapse of a recovery time, then inferring that a health level of the user of the one discretely-mobile wireless parameter-sensing node that underwent motion substantially immediately after an acceleration-event is substantially deteriorated; and
wherein adaptively instructing one or more discretely-mobile wireless parameter-sensing nodes to execute one or more tasks that are responsive to results of the analysis further comprises:
determining one or more tasks that are appropriate to the deteriorated health level.
9. The method of claim 6 , wherein the tasks comprise tasks that are responsive to one or more common events, the one or more common events comprising one or more of:
requesting a user of a given node effected by the one or more common events to interact with a man-machine interface on the given node;
streaming a video signal from a given node effected by the one or more common events to the remote host; and
instructing a user of a node not effected by the one or more common events albeit proximate to the given node effected by the one or more common events to inspect the health of a user of the node effected by the one or more common events.
10. The method of claim 9 , wherein adaptively instructing the one or more discretely-mobile wireless parameter-sensing nodes to execute one or more tasks that are responsive to results of the analysis further comprises:
instructing at least one of the first and second tasks;
receiving the resultant data, respectively; and
determining, from the resultant data, whether to instruct the third task.