Patent Publication Number: US-11037670-B2

Title: Activity assistance system

Description:
This application claims the benefit of U.S. Provisional Application No. 62/901,374 filed Sep. 17, 2019 and titled “ACTIVITY ASSISTANCE SYSTEM”. U.S. Provisional Application No. 62/901,374 filed Sep. 17, 2019 and titled “ACTIVITY ASSISTANCE SYSTEM” is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     The following relates to the activity assistance arts, the rehabilitation therapy arts, activities of daily life (ADL) assistance arts, disability assessment for cognitive and/or motor disorders such as traumatic brain injury (TBI), Alzheimer&#39;s disease, brain lesions, stroke, or the like, and the like. 
     Rehabilitation therapy is a crucial recovery component for numerous medical conditions. For example, every year, more than 200,000 Traumatic Brain Injury (TBI) cases are reported in the United States alone. Many patients with TBI suffer cognitive impairment that affects their ability to interact with their environments and objects of daily living, preventing them from living independently. Approaches for TBI rehabilitation includes mirror therapy and therapist guided exercises. Since TBI is such a diffuse injury, these therapies only help some patients, and require therapist time which may be limited by insurance reimbursement or other practical considerations. More generally, rehabilitation therapy is commonly employed in persons suffering from agnosia (difficulty in processing sensory information) or apraxia (motor disorders hindering motor planning to perform tasks). Besides TBI, these conditions can be caused by conditions such as Alzheimer&#39;s disease, brain lesions, stroke, or so forth. 
     Certain improvements are disclosed herein. 
     BRIEF SUMMARY 
     In accordance with some illustrative embodiments disclosed herein, an activity assistance system includes a video camera arranged to acquire video of a person performing an activity, an output device configured to output human-perceptible prompts, and an electronic processor programmed to execute an activity script. The script comprises a sequence of steps choreographing the activity. The execution of each step includes presenting a prompt via the output device and detecting an event or sequence of events subsequent to the presenting of the prompt. Each event is detected by performing object detection on the video to detect one or more objects depicted in the video and applying one or more object-oriented image analysis functions to detect a spatial or temporal arrangement of one or more of the detected objects. Each event detection triggers an action comprising at least one of presenting a prompt via the output device and and/or going to another step of the activity script. 
     In accordance with some illustrative embodiments disclosed herein, an activity assistance method comprises: using a video camera, acquiring video of a person performing an activity; using an electronic processor, executing an activity script comprising a sequence of steps choreographing the activity wherein the execution of each step includes presenting a prompt via an output device and detecting an event or sequence of events subsequent to the presenting of the prompt, wherein each event is detected by performing object detection on the video to detect one or more objects depicted in the video and applying one or more object-oriented image analysis functions to detect a spatial or temporal arrangement of one or more of the detected objects; and responsive to each event detection, performing an action comprising at least one of presenting a prompt via the output device and and/or going to another step of the activity script. 
     In accordance with some illustrative embodiments disclosed herein, a non-transitory storage medium stores instructions readable and executable by an electronic processor to perform an activity assistance method comprising: receiving, from a video camera, video of a person performing an activity; executing an activity script comprising a sequence of steps choreographing the activity wherein the execution of each step includes presenting a prompt via an output device comprising one or more of a display and/or a loudspeaker and detecting an event or sequence of events subsequent to the presenting of the prompt, wherein each event is detected by performing object detection on the video to detect one or more objects depicted in the video and applying one or more object-oriented image analysis functions to detect a spatial or temporal arrangement of one or more of the detected objects; and responsive to each event detection, performing an action comprising at least one of presenting a prompt via the output device and and/or going to another step of the activity script. 
     In accordance with further embodiments and/or variants of the aforementioned embodiments, the disclosed activity assistance methods and systems may be employed for quantitative diagnosis of cognitive and/or motor disorders such as traumatic brain injury (TBI), Alzheimer&#39;s disease, brain lesions, stroke, or the like based on functional performance of tasks. In such embodiments or variants, the activity assistance system is programmed with task-oriented activities that allows individuals with (for example) mild to severe TBI to be assessed based on functional activity. Performance-based assessments in which the subject completes a complex task using real-world functional objects can be more sensitive to subtle cognitive impairment than traditional impairment-based measures. The activity assistance system suitably tracks the number of user errors, speed, sequencing ability, coordination, response times, and other meaningful metrics related to assessment of cognitive and/or motor skills status. Task difficulty and depth of feedback may be configurable and vary depending on the individual&#39;s injury and ability. Results of the assessment are suitably statistically analyzed and compiled in a performance report that informs the TBI (or other) diagnosis. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Any quantitative dimensions shown in the drawing are to be understood as non-limiting illustrative examples. Unless otherwise indicated, the drawings are not to scale; if any aspect of the drawings is indicated as being to scale, the illustrated scale is to be understood as non-limiting illustrative example. 
         FIG. 1  diagrammatically shows an activity assistance system. 
         FIGS. 2A-15A  diagrammatically show a progression of prompt dialog content presented by the activity assistance system of  FIG. 1  for performing the activity of making a peanut butter &amp; jelly (PB&amp;J) sandwich. 
         FIGS. 2B-15B  diagrammatically show images of a subject performing the activity of making a PB&amp;J sandwich acquired by the video camera of the system of  FIG. 1  at times corresponding to presentation by the system of the prompt dialog content of respective  FIGS. 2A-15A . 
         FIG. 16  diagrammatically shows an illustrative display presenting a performance evaluation for the activity of making a PB&amp;J sandwich described with reference to  FIGS. 2A-15A and 2B-15B . 
         FIG. 17  plots an activity performance comparison between healthy subject and severe TBI subject. 
         FIG. 18  diagrammatically shows an activity assistance system similar to that of  FIG. 1 , but in which the object detection delineated by bounding boxes of the embodiment of  FIG. 1  is replaced by instance segmentation in the embodiment of  FIG. 18 . 
     
    
    
     DETAILED DESCRIPTION 
     Physical and occupational therapists are commonly employed to guide a patient (or, more generally, a person) in performing various rehabilitation therapy activities. Many of these activities correspond to (or actually are) activities of daily living (ADLs) such as making a sandwich, brushing teeth, combing hair, or so forth. Since competence in performing various ADLs is essential for the patient to be able to live independently (or at least with limited assistance), ADLs performed under the guidance of the physical therapist are ideal rehabilitation therapy activities. 
     The usual approach of employing a physical therapist to guide a patient through rehabilitation therapy activities is problematic due to cost. Additionally, in cases in which the physical therapist cannot perform home visits, the rehabilitation therapy activities must be performed at a hospital or other centralized location. This may be difficult or impossible if the patient lacks adequate transportation. Even if the patient can come to the hospital, the unfamiliar setting may make it more difficult for the patient to perform an ADL. These limitations can lead to reduced therapy time, which reduces effectiveness of the therapy. Another difficulty with employing a physical therapist is that for certain ADLs, such as toileting, the patient may be uncomfortable having a physical therapist present while the patient performs the activity. 
     Another possible approach would be to employ a virtual reality (VR) system for performing the rehabilitation therapy activities. Commercial VR systems are relatively inexpensive, and can be programmed to simulate various rehabilitation therapy activities. However, VR systems may not provide sufficiently realistic feedback to the patient. The VR environment may differ significantly from the patient&#39;s home environment, which may hinder the patient&#39;s progress. Furthermore, sensory inputs may be imperfectly simulated by the VR system. In particular, tactile feedback in a VR system is typically crude or nonexistent. Tactile feedback is of importance for many ADLs. For example, a patient with agnosia may be more likely to correctly recognize an object if the patient has both visual and tactile sensory feedback. Using a VR system also usually involves wearing a headset and VR gloves with tactile sensors, again making the VR environment less than fully familiar and realistic to the patient. A further problem is that a patient who has difficulty with ADLs in general may have difficulty successfully putting VR gear on. 
     Disclosed herein are rehabilitation therapy systems (or, more generally, activity assistance systems) that can be deployed in the patient&#39;s home (or additionally or alternatively in a hospital, e.g. for the patient to receive additional therapy during in-patient stay), and which operate in the real world (rather than in VR) and preferably in the patient&#39;s home and using the patient&#39;s own objects. The disclosed approaches leverage the fact that performing most ADLs require manipulation of, or contact with, a small set of objects. For example, the ADL of brushing teeth may involve as few as four objects: a toothbrush, toothpaste, a faucet handle, and a water stream (which comes into existence when the faucet handle is turned on). The ADL of combing hair may involve only two items: a comb or brush, and the patient&#39;s head. The disclosed approaches further leverage the fact that, in performing most ADLs, there is a small and discrete number of mistakes the patient is likely to make. For example, a patient with agnosia is most likely to make a mistake in which one object of the small set of objects is mistaken for another object of that small set. If the set size is five objects then there are only 
               (         5           2         )     =     1   ⁢   0           
theoretically possible ways of confusing two objects, some of which may be improbable or impossible in a given ADL. For example, in the case of the tooth brushing ADL having four objects, there are theoretically six possible object confusion mistakes, but by far the most probable one is confusing the toothbrush and toothpaste. For a patient with apraxia, most common mistakes are to mis-order objects, or perform a sequence of operations in the wrong order. For five objects, there are 5!=120 possible orders, but again many of these may be unlikely in a given ADL, so that there is a limited number of ways ordering mistakes can be made for a particular ADL. Yet a further insight is that a typical ADL is sequential in nature, with only a relatively small number of well-defined steps and little or no branching. For example, the brushing teeth ADL may entail the sequential steps of: pick up toothbrush; place toothbrush under water briefly; pick up toothpaste; apply toothpaste to toothbrush; brush teeth using toothbrush; place toothbrush under water briefly; and replace toothbrush.
 
     With reference to  FIG. 1 , based on these insights, an illustrative activity assistance system for use by a patient P (or, more generally, person receiving the assistance) uses video camera-based object recognition to identify the objects involved in the ADL (or, more generally, the rehabilitation therapy activity, or even more generally, the activity) in video V captured by the video camera. The video camera may, for example, be a video camera of smart glasses  10  worn by the patient, or in an alternative embodiment the video camera may be a webcam  12  of a notebook computer  14  that hosts the rehabilitation therapy system or an external webcam that is mounted in the room to view the therapy setting. The smart glasses  10  include a built-in video camera that captures images from the vantage of the patient P wearing the smart glasses  10 , and a transparent display mounted on the lenses of the smart glasses  10  (or, alternatively, the display of the smart glasses  10  may be an opaque display positioned at a perimeter of the lenses). It will also be appreciated that the video camera may employ another imaging modality besides visual, such as LIDAR, infrared imaging, or so forth. The illustrative rehabilitation therapy system further includes at least one output device for presenting prompts for performing the ADL to the patient, or for presenting other information to the patient. The at least one output device may include the mentioned display of the smart glasses  10 , and/or a display  16  of the notebook computer  14 , and/or a loudspeaker of the smart glasses  10  or of the notebook computer  14 . By way of non-limiting illustration, a prompt may be presented as: natural language audio (e.g., using speech synthesis played on a loudspeaker); natural language text displayed on the display  16  of the computer  14 ; natural language text superimposed on the patient&#39;s field of view (FOV) via the AR display of the smart glasses  10 ; an image, graphic, or the like displayed on the display  16  of the computer  14 ; an image, graphic, or the like superimposed on the patient&#39;s field of view (FOV) via the AR display of the smart glasses  10 ; various combinations thereof; and/or so forth. 
     The illustrative notebook computer  14  is programmed to perform a computerized rehabilitation therapy method  20  diagrammatically shown in  FIG. 1  by way of a block diagram. Particularly, the computer  14  is programmed to perform: object detection  22  which identifies objects in frames of the video V and delineates the location of each object in the frame by (in the embodiment of  FIG. 1 ) a bounding box (BB)  24 ; and object tracking  26  across successive image frames, thereby generating object trajectories  28  suitably represented as the BB of each identified object as a function of time. The computer  14  is further programmed to perform object-oriented image analysis functions, such as (in the illustrative example) an object location in-range function  30  (detecting whether an object is within a defined spatial range), an object overlap detection function  32  (detecting whether two objects overlap in space from the vantage of the video camera), and an object order detection function  34 . These object-oriented image analysis functions  30 ,  32 ,  34  provide the ability to detect a wide range of errors in manipulating objects during performance of a typical ADL, particularly errors of the types typically made by patients suffering from agnosia or apraxia. 
     To provide rehabilitation therapy for a particular ADL, the computerized rehabilitation therapy method  20  further includes executing an activity script  40  by the computer  14 . The activity script  40  choreographs an ordered sequence of steps making up the ADL. The execution of each step of the sequence includes presenting a human-perceptible prompt and detecting an event or sequence of events subsequent to the presenting of the prompt. The detected events trigger actions, and hence are referred to as on_event→action responses. (It is noted that while the detect event is typically a positive event which actually occurs, the event of some on_event→action responses may be a negative event, that is, an event which does not occur within a specified time frame. For example, if the patient is asked to pick up an object, an on_event→action response may comprise the event of failing to detect the patient picking up the object in, for example, 5 seconds, and the resulting action may for example be to send a new, perhaps more detailed, prompt instructing the patient to pick up the object). As already described, the prompts may be presented as natural language audio, natural language text, images, graphics, various combinations thereof, and/or so forth. For example, if the first step calls for the patient P to place a set of objects into a particular order, then the prompt may be the natural language spoken and/or displayed text “Please place the objects shown into the order shown” together with an image or graphical depiction of the objects in the desired order presented as AR content via the smart glasses  10  or as a 2D image shown on the display  16 . 
     Each on_event→action response is triggered by an event detected by performing the object detection  22  on the video V to detect one or more objects depicted in the video V and applying at least one of the one or more object-oriented image analysis functions  30 ,  32 ,  34  to detect a spatial or temporal arrangement of one or more of the detected objects. The detection of an event triggers an action such as providing an additional prompt, and/or going to another step of the activity script  40 . To continue the immediate example, if the object order detection function  34  detects that the objects are laid out by the patient P in the wrong order (the “on_event”) then the action part of the response may be to display a further prompt indicating the error and asking the patient P to correct the error. On the other hand, if the object order detection function  34  detects that the objects are laid out by the patient P in the correct order (the “on_event”) then the action part of the response may be to display a further prompt congratulating the patient P for this success. 
     In general, a sequence of on_event→action responses may occur, as driven by the events observed in the video V using the object-oriented image analysis functions  30 ,  32 ,  34 . For example, the patient P may initially place the objects in the wrong order (first “on_event”) triggering the corrective prompt response; then, the patient P may correct the ordering of the objects (second “on_event”) triggering the congratulatory prompt response. It is also contemplated for an on_event→action response to entail recursively returning to a previous step. For example, the first step may be for the patient to place the objects on the table T, and the second step may be for the patient to order the objects in a particular order. If, at the second step, the patient knocks an object off the table T (an “on_event” suitably detected by the object location in-range function  30  not being located anywhere in the video frame) then the response may be to go back to the first step. Furthermore, for a more complex ADL, an on_event→action response may produce a branching in the choreographed flow of the ADL, e.g. of the form “on_event1→goto step x”; “on_event2→goto step y”. 
     Upon completion of the activity script  40  (and, hence, completion of the ADL choreographed by that script  40 ), a performance evaluation  42  preferably analyzes the performance of the patient P. This analysis can, for example, count the total number of “on_event→action” responses that correspond to correct actions by the patient P versus a count of the total number of “on_event→action” responses that correspond to incorrect actions by the patient P. Optionally, this may be further broken down, e.g. distinguishing between “on_event→action” responses that correspond to incorrect object identification versus “on_event→action” responses that correspond to incorrect object ordering. Other performance metrics can be employed, such as total completion time, optionally broken down into completion times for various different steps. 
     Optionally, the video V, or portions thereof, may be saved on a non-transitory storage medium for later review by the patient&#39;s physician. Such recordation, if done at all, should be done in compliance with applicable patient privacy regulations and only with the consent of the patient P or the patient&#39;s legal guardian. 
     The illustrative rehabilitation therapy system includes a library  44  of activity scripts for different ADLs. By way of non-limiting illustration, the library  44  may include activity scripts correlating ADLs such as making a sandwich, brushing teeth, taking one or more medications, combing hair, toileting, trash removal, cooking tasks, grocery shopping tasks, ironing, pumpkin carving, present wrapping, picture framing, or so forth. Advantageously, a wide range of different ADLs can be supported merely by constructing a suitable script for each ADL. Constructing a script entails identifying the sequence of steps making up the ADL, and for each event adding an appropriate prompt and appropriate “on_event→action” responses. The prompts can be synthesized audio speech, textual natural language content presented on a display, and/or images or graphical representations. For example, a prompt asking the person P to arrange a set of objects in a specified order can include an image of the objects in that order, or can include a graphical representation of the objects in that order (for example, constructed using a the Blender modeling toolset (available from the Blender Foundation). In one suitable embodiment, MATLAB or Python scripts are programmed, including a master script that calls on selected activities. The activities have information regarding the sequences, prompts, and error/correct responses. Each step in the sequence is linked to one or more object-oriented image analysis functions  30 ,  32 ,  34 . Each step is iterated through in the master script and, based on the type of action detected, the master script determines the effect. This allows the system to generalize to many activities. The object-oriented image analysis functions  30 ,  32 ,  34  are typically custom built, using matrix operations on the bounding boxes  24  that are generated from the CNN  22 . For the object location in-range function  30 , the center of the bounding box is measured from the edges of the image in pixels. A confidence bound is set to allow for some error in positioning. A suitable boundary such as a mat (see, e.g.  FIG. 2B ) can also be tracked and used to reference the object locations based on the bounding box. For the object overlap detection function  32 , the area of overlap between bounding boxes is calculated as well as the distances between corners to calculate overlap. For the object order detection function  34 , each bounding box is linked to an object, so the corner of each bounding box is used to determine the ordering of objects. The x-axis location, in pixels, is used. These are merely illustrative examples of object-oriented image analysis functions. As still yet another non-limiting example (not shown), an object touching operation can be applied to determine whether the user&#39;s hand is touching/holding an object. This suitably uses similar logic to the object overlap detection function  32 , but does so with a model that detects the objects and a model that detect the user&#39;s hand. Both models run in parallel and use similar architectures for detection and bounding box generation. The CNN  22  can be a standard off-the-shelf neural network, and is optionally retrained with task-specific images using transfer learning. To maximize accuracy for an activity script, the CNN  22  is preferably trained to detect objects of the set of objects involved in the script. Alternatively, the rehabilitation therapy system may be designed to provide rehabilitation therapy assistance for a single ADL, in which case only a single activity script is needed and the library  44  is suitably omitted. 
     The illustrative rehabilitation therapy system is triggered by an auto-start function  46  which monitors the webcam  12  and starts the video acquisition and starts running the script  40  upon detection of motion by the webcam  12 . Advantageously, this allows the patient P to start using the illustrative rehabilitation therapy system without taking any affirmative action other than sitting down at the table T. Other auto-start triggers are contemplated, such as starting the computer  14  or the smart glasses  10  (a suitable approach if the computer or smart glasses are only used in the rehabilitation therapy system), performing facial recognition on video acquired by the webcam to detect the face of the patient P, or so forth. Instead of an autostart, the rehabilitation therapy system can be manually started by bringing up a rehabilitation therapy application program on the computer  14  (a suitable approach if, for example, the person P has in-home assistance, or is capable of reliably taking these actions). 
     The illustrative rehabilitation therapy system is merely an example, and numerous variants are contemplated. For example, the system could include only the smart glasses  10 . In this embodiment, the video camera of the smart glasses  10  would serve to provide the video V and the AR display and/or loudspeaker of the smart glasses  10  would present the prompts. Conversely, the system could include only the computer  14 . In this embodiment, the webcam  12  would serve to provide the video V and the display  16  and/or loudspeaker of the computer  14  would present the prompts. In the case of a task involving manipulation of objects on a table, the webcam  12  may be modified as compared with the webcam of a commercial laptop or notebook computer in order to have its field of view (FOV) angled downward to image a surface on which the laptop or notebook computer is disposed when the display  16  is oriented to be viewed by the person P. (By comparison, the webcam of a commercial laptop or notebook computer is typically angled generally forward so as to capture the face of the person P when the person P is viewing the display  16 ). Advantageously, the laptop or notebook computer with the thusly modified webcam provides a portable, single-component system for implementing the activity assistance system, as in this embodiment the smart glasses  10  could be omitted. Further, the illustrative notebook computer  14  could be replaced by a desktop computer, mobile device (e.g. a cellphone or tablet computer, preferably mounted in a dock), and/or so forth. In other embodiments, other hardware arrangements may be used. For example, to assist in a tooth brushing ADL, the video camera may optionally be mounted on the bathroom wall and the display may be integrated into a bathroom mirror. The illustrative object-oriented image analysis functions  30 ,  32 ,  34  can be replaced and/or augmented by other object-oriented image analysis functions, such as an in-front-of detection function that detects when an object A is in front of an object B, or a reciprocation detection function that detects when an object is moving back-and-forth (useful, for example, in constructing a tooth brushing ADL script). 
     The rehabilitation therapy system comprises, in part, an electronic processor programmed to perform the computerized rehabilitation therapy method  20 . The electronic processor may include the electronic processor of the computer  14  and/or the electronic processor of the smart glasses  10 . Optionally, some portions of the computerized rehabilitation therapy method  20  may be performed by a cloud computing resource comprising ad hoc connected Internet-based server computers. The computerized rehabilitation therapy method  20  is suitably embodied as a non-transitory storage medium storing instructions which are readable and executable by such a processor to perform the computerized rehabilitation therapy method  20  in conjunction with a video camera for acquiring the video V and an output device for presenting the prompts. By way of non-limiting illustrative example, the non-transitory storage medium may comprise a hard disk or other magnetic storage medium, an optical disk or other optical storage medium, a solid state drive (SSD) or other electronic storage medium, or various combinations thereof. 
     Advantageously, the disclosed rehabilitation therapy systems can be set up in the patient&#39;s home with very limited hardware (e.g., the notebook computer  14  and/or the smart glasses  10  in the illustrative example). The object detector  22  may comprise an available artificial intelligence (AI) based object recognition module such as ResNet-50 which employs a convolutional neural network (CNN) trained on images from the ImageNet database and using a YOLO (You Only Look Once) framework in which the entire image is processed once, as a whole, by the CNN. While standard video runs at 30 frames/second (30 fps), for the disclosed rehabilitation therapy systems, the frame rate can optionally be lowered to as low as a few frames per second, which can facilitate object recognition processing performed on a frame-by-frame basis as each frame is acquired. Furthermore, in a typical ADL the patient P handles a small, finite number of discrete objects, usually 5-10 objects or less. For this closed universe of 5-10 objects, transfer learning can be used to tailor the CNN to the specific objects involved in the activity, and to further enhance accuracy. An off-the-shelf object recognition CNN may not be trained to recognize the objects involved in the activity, or may only be trained to recognize generic objects. For example, an off-the-shelf CNN that is trained to recognize a generic “jar” is unsuitable for a peanut butter-and-jelly sandwich making task in which the peanut butter jar and the jelly jar must be differentiated. By using a color video camera, color features can also be employed in the object recognition. As a consequence, it is expected that object recognition accuracy of close to 100% can be readily achieved for the objects handled by the patient P in most ADLs, along with high concomitant rejection (i.e. ignoring) of non-relevant objects that are not involved in the activity of the activity script. 
     In general, the rehabilitation therapy system includes a video camera (e.g., the video camera of the smart glasses  10 , or the webcam  12  of the computer  14 ) arranged to acquire video V of the person P performing an activity (typically an ADL); an output device configured to output human-perceptible prompts (e.g., the display  16  of the computer  14 , and/or the display of the smart glasses  10 , and/or a loudspeaker of the computer  14 , and/or a loudspeaker of the smart glasses  10 ; and an electronic processor (e.g., the electronic processor of the computer  14  and/or the electronic processor of the smart glasses  10  and/or electronic processors of an Internet-based cloud computing resource). The electronic processor is programmed to execute the activity script  40  comprising a sequence of steps choreographing the activity. The execution of each step includes presenting a prompt via the output device and detecting an event or sequence of events subsequent to the presenting of the prompt. Each event is detected by performing object recognition on the video V to detect one or more objects depicted in the video (e.g., via operations  22 ,  26 ) and applying one or more object-oriented image analysis functions  30 ,  32 ,  34  to detect a spatial or temporal arrangement of one or more of the detected objects. Each event detection triggers an action comprising at least one of presenting a prompt via the output device and and/or going to another step of the activity script  40 . 
     With reference now to  FIGS. 2A-15A  and  FIGS. 2B-15B , an illustrative rehabilitation therapy method suitably performed by the rehabilitation therapy system of  FIG. 1  is described. The illustrative example executes an activity script for the ADL of making a peanut butter and jelly (PB &amp; J) sandwich. In the examples, the video camera of the smart glasses  10  is used to acquire the video V, the electronic processor of the computer  14  executes the computerized rehabilitation therapy method  20 , and the display  16  of the computer  14  is used as the output device. To illustrate the method,  FIGS. 2A-15A  illustrate the prompts presented on the display  16  for successive steps of the PB &amp; J sandwich-making ADL, and corresponding  FIGS. 2B-15B  show a representative frame of the video V acquired during the execution of the respective steps of the PB &amp; J sandwich-making activity script. The prompts for the PB &amp; J sandwich-making ADL shown in  FIGS. 2A-15A  include graphical representations of the following objects: a plate  50 , a jar of peanut butter (PB)  52 , a jar of jelly or jam  54 , bread  56  (one or two slices in any given graphical representation), a knife  58 , a spread of PB  60 , and a spread of jelly  62 . The corresponding video frames of  FIGS. 2B-15B  show the corresponding detected objects in the images: a detected plate  50   i , a detected jar of peanut butter (PB)  52   i , a detected jar of jelly or jam  54   i , detected bread  56   i  (one or two detected slices in any video frame), a detected knife  58   i , a detected spread of PB  60   i , and a detected spread of jelly  62   i . It should be noted that the reference symbols  50 ,  52 ,  54 ,  56 ,  58 ,  60 ,  62  are superimposed on the graphical representation and are not part of the graphical representations shown in  FIGS. 2A-14A . Likewise, the reference symbols  50   i ,  52   i ,  54   i ,  56   i ,  58   i ,  60   i ,  62   i  are superimposed on the image frames of  FIGS. 2B-14B  and are not part of actual image frames. 
       FIG. 2A  illustrates an initial prompt asking the patient to place items  50 ,  52 ,  54 ,  56 ,  58  onto the table in the graphically represented order. The video frame of corresponding  FIG. 2B  acquired at the time the prompt of  FIG. 2A  is presented shows that at this point, the patient has not placed any items onto the table. As seen in  FIG. 3B , the patient initially places the items on the table, but in the incorrect order as the peanut butter  52   i  and jelly  54   i  are reversed compared with the order of the peanut butter  52  and jelly  54  shown in the prompt of  FIG. 2A . The object order detection function  34  detects this incorrect order, and the responsive action is to display the prompt shown in  FIG. 3A , which identifies the error by the text: “The order seems a little off. Try moving the items marked with a red X”, and by the indicated “X” marking in the graphical representation of the prompt of  FIG. 3A .  FIG. 4B  shows the image frame after the patient has corrected the ordering by switching the peanut butter and jelly. The object order detection function  34  detects this now correct order, and the responsive action is to display the congratulatory prompt shown in  FIG. 4A  which includes the text “Great job!” and to move to the next step of the activity script. 
     The next step of the PB &amp; J sandwich making activity script presents the prompt shown in  FIG. 5A , which asks the patient to “Place the plate in front of you” with the prompt further including a graphical representation of the plate  50  in the specified location at the center of the table (i.e., “in front of you”). As seen in  FIG. 5B , at the time the prompt of  FIG. 5A  is initially presented the video frame still shows the arrangement achieved by the patient at  FIG. 4B . As seen in  FIG. 6B , the patient erroneously places the bread  56   i  at the specified location, rather than the plate  50   i . The object location in-range function  30  detects the error that the bread is at the specified location, rather than the specified plate.  FIG. 6A  shows the prompt that is presented response to this error detection, which states “You appear to have placed the Bread Slice. Please switch this item.” The prompt also retains the graphical representation of the prompt of  FIG. 5A  showing the plate  50  at the specified location.  FIG. 7B  shows the image frame after the patient corrects the error by switching the plate and the bread. This correct placement of the plate  50   i  in the specified location (“in front of you”) is detected by the object location in-range function  30 , triggering the responsive congratulatory prompt shown in  FIG. 7A  including the text “Great! Now on to the next step”, along with triggering going to the next step of the activity script. 
     The next step includes presenting the prompt shown in  FIG. 8A , which asks the patient to “Place two bread slices on the plate” and provides a graphical representation of the two bread slices  56  on the plate  50 . The image frame shown at  FIG. 8B  corresponding to the time when the prompt of  FIG. 8A  is first presented shows a state similar to that obtained at the frame of  FIG. 7B .  FIG. 9B  shows the image frame acquired after the patient correctly placed the bread slices  56   i  on the plate  50   i . The object overlap detection function  32  detects overlap of the bread slice objects  56   i  and the plate  50   i . As this is the correct overlap of objects, the triggered activity response is a congratulatory prompt shown in  FIG. 9A  including the graphical representation of the two slices of bread  56  on the plate  50  along with the text “Nice work!”, and the triggered activity response also includes going to the next step of the PB &amp; J sandwich making activity script. 
     The next step includes presenting the prompt shown in  FIG. 10A , which asks the patient to “Open the peanut butter jar, then use the knife to spread peanut butter on one slice of bread” and presents a graphical representation of same.  FIG. 10B  shows an image frame corresponding to the time the prompt of  FIG. 10A  is first presented, and the state is similar to that shown by the frame of  FIG. 9B .  FIG. 11B  shows a video frame acquired after the patient successfully performed this operation. The object overlap detection function  32  detects this success as overlap of the peanut butter spread  60   i  and one slice of bread  56   i . (On the other hand, if the patient had incorrectly spread the peanut butter on the plate, not shown, then the object overlap detection function  32  would detect this as an overlap of the peanut butter spread and the plate). The detection of the overlap of the peanut butter spread  60   i  and the bread slice  56   i  triggers an action response including the prompt shown in  FIG. 11A  which includes the congratulatory text “Awesome!” and the same graphical representation as shown in  FIG. 10A , and also includes moving to the next step of the activity script. 
     The next step includes presenting the prompt shown in  FIG. 12A , which asks the patient to “Open the jelly jar, then use the knife to spread jelly on the other slice of bread” and presents a corresponding graphical representation.  FIG. 12B  shows an image frame corresponding to the time the prompt of  FIG. 12A  is first presented, and the state is similar to that shown by the frame of  FIG. 11B .  FIG. 13B  shows a video frame acquired after the patient successfully performed this operation. The object overlap detection function  32  detects this success as overlap of the jelly spread  62   i  and the other slice of bread  56   i . (On the other hand, if the patient had incorrectly spread the jelly on the same slice of bread on which the peanut butter spread is already present, not shown, then the object overlap detection function  32  would detect this as an overlap of the peanut butter spread and the jelly spread). The detection of the overlap of the jelly spread  62   i  and the other bread slice  56   i  triggers an action response including the prompt shown in  FIG. 13A  which includes the congratulatory text “Great! One more step” and the same graphical representation as shown in  FIG. 12A , and also includes moving to the next step of the activity script. 
     The next step includes presenting the prompt shown in  FIG. 14A , which asks the patient to “Press the bread together with the peanut butter and jelly inside, then cut the sandwich in half with the knife” and presents a corresponding graphical representation.  FIG. 14B  shows an image frame corresponding to the time the prompt of  FIG. 14A  is first presented, and the state is similar to that shown by the frame of  FIG. 13B .  FIG. 15B  shows a video frame acquired after the patient successfully performed this operation. In one approach, the object order detection function  34  detects this success as two bread slice halves  56 iH next to each other. (Other approaches could be used. For example, new objects corresponding to half-sandwiches could be recognized at the object recognition stage.) The detection triggers an action response including the prompt shown in  FIG. 15A  which includes the congratulatory text “Awesome job! Enjoy your sandwich” at which point the PB &amp; J sandwich making activity script ends. 
     It will be appreciated that the described execution of the illustrative PB &amp; J sandwich making activity script is merely an example, and that numerous other ADLs can be choreographed by an analogous activity script with suitably tailored prompts and on_event→action detection/triggered response options. For example, in the case of a toothbrushing ADL, the person uses the toothpaste object to dispense a toothpaste spread object onto a toothbrush object, corresponding to the operations of the PB &amp; J activity script choreographed as described with reference to  FIGS. 10A-13A and 10B-13B . This type of operation can be generalized to presenting a prompt via the output device asking a person to dispense a substance onto a specified object, and applying the object overlap detection function  32  to detect the substance overlapping an object. Detection by the object overlap function  32  that the substance overlaps an object other than the specified object triggers presenting a prompt indicating the substance has been applied to an incorrect object and asking that the substance be applied to the specified object; whereas, detection by the object overlap function that the substance overlaps the specified object triggers presenting a prompt congratulating the person on dispensing the substance onto the specified object. The prompt in such cases suitably includes displaying an image or graphical representation of the substance dispensed onto the specified object on the display (e.g. as shown in the prompts of  FIGS. 10A and 12A ). 
     Similarly, in a generalized case an activity script may include presenting a prompt via the output device asking a person to cause an interaction of a first object and a second object, and applying the object overlap detection function  32  to detect whether the first object and the second object overlap. Detection by the object overlap function that the first object and the second object overlap triggers presenting a prompt congratulating the person on causing the interaction of the first object and the second object; whereas, detection of one of the first or second objects overlapping some other object may be taken as a trigger to prompt the person to correct the error. The prompt may suitably include displaying an image or graphical representation of the interaction of the first object and the second object. 
     With reference to  FIG. 16 , after completion of the activity script (i.e., after the final congratulatory prompt as shown in  FIG. 15A  in the illustrative example), the performance evaluation  42  preferably provides a performance report. To this end, the electronic processor is further programmed to track detected events indicating mistakes by the person in performing the activity (e.g., events detected in the image frames of  FIGS. 3B and 6B  in the illustrative example), and upon completion of the execution of the activity script, a performance report is presented including metrics of the person&#39;s performance of the activity determined from the tracked events. Optionally, the electronic processor may be further programmed to quantify times required for the person to perform aspects of the activity based on time intervals between execution of successive steps of the sequence of steps choreographing the activity, and the presented performance report then further includes metrics of the person&#39;s performance of the activity determined from the quantified times.  FIG. 16  shows an example of a possible performance report. 
     The disclosed activity assistance systems and methods operate in the real world, using actual objects of the patient (or more generally, the person) to perform the actual ADL, rehabilitation therapy activity, or other activity (as opposed to using a VR system), preferably in the person&#39;s own residence (as opposed to at a hospital or other central medical facility). As such, it will be appreciated that the disclosed activity assistance systems can be used in therapeutic or rehabilitation mode, that is, providing a person with practice in performing a scripted ADL or rehabilitation activity. Additionally or alternatively, the disclosed activity assistance systems can be used in assistive mode, that is, providing a person with assistance in performing a scripted ADL as part of the person&#39;s daily living. 
     In addition to assisting in rehabilitation of TBI or other brain diseases, for example the illustrative case of assisting with the peanut butter and jelly sandwich making task as described with reference to  FIGS. 2A and 2B  through  FIG. 16 , the disclosed activity assistance system and corresponding methods can also be used in diagnosing or assessing severity of TBI or other brain diseases. This allows for objectively diagnosing (e.g.) TBI severity based on cognitive function. The quantitative diagnosis is suitably based on functional performance of relevant tasks, and can be used in mobile settings such as assessing possible TBI in an injured soldier in a combat situation. This is expected to improve confidence in combat medicine decisions in early intervention of mild to moderate TBI, and improve consistency in medical care. This portable platform for TBI diagnosis suitably uses object detection and interactive scripts to guide individuals through functional activities using physical objects and quantifies performance for accurate diagnosis. 
     An estimated 5.3 million Americans currently live with a TBI-related disability. Combat-related exposures, as well as routine operational and training activities, put military service members at increased risk of sustaining a TBI with an average of 20,000 U.S. military service members reporting a TBI each year. Despite the high incidence of TBI in military settings, there is no universally accepted battery of assessments to holistically characterize TBI severity. The Glasgow Coma Scale (GCS) is a commonly used screening tool to determine severity of TBI in the acute phase of injury, however, it lacks the sensitivity and specificity to identify clinically relevant cognitive impairment that may impact safety and function in a demanding military setting. Furthermore, while the GCS measures basic physiological response (e.g., withdrawal from noxious stimuli), it fails to quantify functional cognitive deficits associated with TBI, which is an important metric for determining a soldier&#39;s ability to safely return to active duty. It is well-established that functional deficits during complex activities and work tasks are underdiagnosed and undertreated in individuals with TBI, yet there is presently no widely accepted assessment of functional cognition post-TBI. Hence, there is an unfulfilled need to develop diagnostic tools that characterize the functional deficits associated with TBI, particularly for military personnel preparing to return to active duty. 
     Diagnosing TBI severity and readiness to return to active duty is an inherently complex task. It is further complicated in military settings such as battalion aid stations, where time and resources are limited. A basic physical examination of motor function, coordination, reflexes, or so forth is easily conducted in such a forward military setting, but this does not accurately or consistently diagnose mild to moderate TBI. Further, use of currently available TBI assessment tools such as basic neurological exams (e.g., electroencephalogram) or diagnostic imaging (e.g.: computed tomography or magnetic resonance imaging scans) require dedicated equipment, which is prohibitive in forward military settings in which rapid decisions must be made with limited resources. Ideally, in addition to a physical examination, a battery of neuropsychological tests are administered to assess executive functions (e.g., memory, attention) of individuals with TBI. While valuable for identifying isolated cognitive impairments, neuropsychological tests often fail to capture functional performance deficits, such as those required to do highly complex work tasks. This is due to the qualitative nature of scoring criteria, variability in the assessors themselves, and the limited time assessors are able to devote to each patient due to environment or medical staff availability. Additionally, commonly used impairment-based assessments evaluate single-component cognitive processes in non-distracting and non-stressful environments, they fail to replicate the demands of real-world military environments and tasks. This has led to mild to moderate cognitive impairments, such as slower reaction times and increased task errors, on complex dual tasks (e.g., loading ammunition into a magazine while listening for radio commands) sometimes going undiagnosed. These deficits may lead to decreased safety, inability to complete missions, or increased incidence of injury. In order to objectively measure a soldier&#39;s performance in a way that is ecologically valid, an assessment should simulate the vocational demands of military tasks, demonstrate complexity adequate to account for fluid conditions in an operational environment, and challenge known TBI-related vulnerabilities. The disclosed activity assistance system advantageously can be used to diagnose and assess severity of mild to moderate TBI and provides a portable, efficient, and function-focused assessment to improve consistency in characterizing and diagnosing TBI severity of military personnel, resulting in metric-based data measures for return to active duty decision making. 
     Using the object detection  22  to detect specific objects, the activity assistance system of  FIG. 1  identifies and tracks real world objects being manipulated around a work surface or room and assesses human-object interactions. This provides a portable system by using objects readily deployed in the operational environment without the need to add more footprint, and enables administration of an ecologically valid assessment tool that simulates the complex vocational demands of military tasks in an operational environment. The activity assistance system is suitably programmed (e.g., by suitable activity scripts stored in the activity scripts library  44 ) with task-oriented activities (e.g., military task-oriented activities such as loading a magazine of a firearm with bullets, disassembling and reassembling a firearm, or so forth) allowing individuals with mild to severe TBI to be autonomously assessed on functional activity that directly apply to their lifestyle and/or occupation. Performance-based assessments where subjects complete complex tasks using real-world functional objects is expected to be sensitive to subtle cognitive impairment, such as may be present with mild to moderate TBI. Example functional activities include loading a firearm magazine, assembling a weapon, organizing pills and medications, making a sandwich (e.g., per  FIGS. 2A and 2B through 16 ), and/or so forth. Use of real-world objects available across most military settings will not only improve the ecological validity of the TBI diagnosis or assessment performed using the activity assistance system, but will also improve ease of use and implementation with little additional equipment required for administration. 
     With reference to  FIG. 17 , evaluation data obtained during a proof-of-concept evaluation with a healthy subject versus a subject with severe TBI are presented. In this evaluation, evaluation data were collected on a patient with severe TBI and a healthy participant performing the same activity with the activity assistance system. For a given activity the activity assistance system prompts the user to perform the steps necessary to complete the exercise. As the user attempts the prompted activity, the system acquires, processes, and interprets frame-by-frame images from the video stream as described with reference to  FIG. 1  (or alternatively, the variant system of  FIG. 18  to be described) to locate and identify all relevant objects in space. Detected objects include the user&#39;s hands, and an activity script  44  was tailored to detect specific military devices, weapons, and other objects commonly used in duty. The system tracks the location of these objects. The goal is for the system to evaluate the user&#39;s interactions with the objects in real-time and tracking the number of user errors, speed, sequencing ability, coordination, and other meaningful metrics. While the activity assistance system is able to calculate these various metrics, the data collected in this proof-of-concept evaluation was not perfect, and some manual adjustments were made. These metrics are correlated to currently accepted assessment scales, used to assess cognitive impairment in TBI at higher roles of care settings, and provide a quantitative and highly detailed assessment of the patient&#39;s cognitive function. Detailed assessment data of the patient cognitive function is critical for detecting subtle cognitive deficits and gaining a better understanding of an individual&#39;s injury and the associated effects. The results of the assessment are statistically analyzed and compiled in a performance report that informs TBI diagnosis. 
     The activity assistance system provides real-time visual and auditory feedback to the participant based on their performance. Task difficulty and depth of feedback are configurable and vary depending on the individual&#39;s injury and ability. Tasks may also be made more challenging by including auditory or visual distractors, or by requiring the individual to multi-task in a complex environment (e.g., load a weapon while simultaneously listening for and responding to commands on a radio). In one approach, activity scripts  44  are provided for three activities with varying degrees of difficulty, to enable assessments to be made in various forward military settings and across a broad spectrum of mild to moderate TBI diagnoses. 
       FIG. 17  shows performance metrics that factor in elements across steps to evaluate cognitive capabilities including spatial coordination, logical ordering, and reaction time. The reaction time for an action was calculated as the time between when the user was prompted to engage an object and the time when the user engaged with the object. (Again, while the activity assistance system is able to calculate the reaction time, the data collected in this initial proof-of-concept evaluation was not perfect and some manual adjustments were made). There were clear differences in performance between an individual with a severe TBI and a healthy participant, and the activity assistance system was able to capture these differences with high accuracy and precision. Further, when compared against current assessment standards including the modified functional independence measure, the data acquired by the activity assistance system confirmed that the subject exhibited both problem-solving deficits and visual neglect. The ability of the activity assistance system to detect cognitive performance subtilties illustrates its ability to supply a detailed, autonomous assessment and supports feasibility of a faster, more consistent diagnoses, regardless of the military domain, geographic location, or the TBI evaluator&#39;s background and training. 
     More generally, the disclosed activity assistance system is expected to find application in various areas of telehealth, especially in forward military settings. This may, for example, allow non-medical personnel to use the activity assistance system to evaluate their peers in austere environments aided by remote medics or clinicians. As the activity assistance device provides metrics that one can easily compare against an adopted baseline, outposts with little more than tactical communications can benefit from this tool by engaging remote medics or clinicians which talk them through the patient&#39;s assessment. For those outposts with satellite communications, they can directly involve these medics and clinicians in the entire process. Connecting the activity assistance system of  FIG. 1  (or  FIG. 18  to be described) to a remote server via satellite would enable remote medics to interpret performance results in real time. Locally stored video recordings and performance metrics can be transmitted to the remote medics or clinicians. This may entail integrating a satellite communication system in the activity assistance system to enable telecommunication with remote medics or military clinicians. 
     Moreover, it will be appreciated that the activity assistance systems and methods disclosed herein will find application in areas beyond assisting a person in performing an ADL or rehabilitation activity. For example, the disclosed activity assistance systems and methods may be applied in the context of an assembly line task, equipment servicing task, meal preparation task, culinary recipe execution task, child education task, or other task that is amenable to scripting, In some activity assistance tasks, the presentation of a congratulatory prompt when an event detection indicates a step is successfully completed may be omitted. For example, in an assembly line task the system may execute an activity script choreographing the assembly line task, in which execution of each step includes presenting a prompt via the output device and detecting an event or sequence of events subsequent to the presenting of the prompt. Detection of an error then suitably triggers presenting a prompt indicating the error and asking that a correction be made. But, in the assembly line task, it may be undesirable to present a congratulatory prompt when an event detection indicates the step is successfully completed, since the expectation is that the steps will usually be successfully completed. In a variant approach, congratulatory prompts may be presented randomly or pseudorandomly, in order to provide encouragement without becoming annoying. 
     With reference back to  FIG. 1 , the object detection performed by the operation  22  employs CNNs that are trained to detect specific objects delineated by bounding boxes. This type of object detection is fast and flexible, as different CNNs can be trained to detect various types of objects. However, this object detection approach has some disadvantages, including that the delineation of the object by the bounding box is imprecise. The CNN-based approach may also have difficulty detecting objects that are partially occluded, as the partially occluded object has a different shape than the objects used for training (although this can be counteracted in some cases by including training examples of partially occluded objects when training the CNN). The imprecision of bounding box delineation of objects also increases the likelihood that the bounding boxes of neighboring objects may overlap, which can make discrimination of (for example) which object is in front versus which object is behind difficult. 
     With reference to  FIG. 18 , another embodiment of the illustrative activity assistance system of  FIG. 1  is shown, which is identical with the activity assistance system of  FIG. 1  except that the CNN-based object detection  22  of  FIG. 1  is replaced in the embodiment of  FIG. 18  by object detection using instance segmentation  122 ; and accordingly block  24  of  FIG. 1  in which objects in each frame are identified by bounding boxes is replaced in the embodiment of  FIG. 18  by a block  124  of  FIG. 18  in which objects in each frame are identified by object pixel boundaries, and block  28  of  FIG. 1  in which object trajectories are identified as (positions of) bounding boxes as a function of time is replaced in the embodiment of  FIG. 18  by a block  128  of  FIG. 18  in which object trajectories are identified as (positions of) pixel boundaries of the objects as a function of time. 
     The object detection using instance segmentation  122  employs an approach in which pixels are classified by object type and object instances are differentiated. Instance segmentation can provide object orientation and high-detail resolution by detecting exact pixel-boundaries of objects. There are a range of instance segmentation techniques known in the image processing arts (e.g., pixel classification followed by blob connectivity analysis; or instance segmentation using mask regional CNNs trained for specific object types (see He et al., “Mask R-CNN”, arXiv:1703.06870v3 [cs.CV] 24 Jan. 2018), and the instance segmentation  122  of  FIG. 18  can employ any such technique. Other object identification techniques known in the image processing arts including blob detection and template matching can be used to identify standardized objects. These methods may be used in place of, or in combination with, object detection methods using CNNs depending on the types of objects used in a task. 
     In the following, an example of using the activity assistance system of  FIG. 18  is described with reference to the activity of Assessment of Military Multitasking performance (AMMP). The AMMP assessment includes a battery of military-specific, functional tasks that require varying levels of complex cognitive processing. In the example here presented, an AMMP task calls for a soldier to load bullets into a firearm magazine. (More generally, the AMMP task may include a weapon disassembly/assembly task or other military-related AMMP task). In such a task, the bullets may be scattered on a table in various orientations. Furthermore, as the soldier inserts a bullet into the magazine, the bullet may be occluded by the soldier&#39;s fingers or (as it enters the magazine) by the magazine itself (or conversely the bullet may occlude the magazine). Object detection using bounding boxes can have difficulty handling these complex object orientations and potential occlusions. 
     A particular advantage of employing instance segmentation to perform the object detection on video frames is that it provides information on the object orientation and can also provide information for extraction occlusion relationships (e.g., does object A occlude object B, i.e. is object A in front of object B?; or, does object B occlude object A, i.e. is object B in front of object A?). For example, in the magazine loading AMMP task, the object detection  22  of  FIG. 1  employs object recognition CNNs that delineate objects by bounding boxes. The bounding boxes determined by the object detection therefore delineate the magazine by a bounding box and delineate bullets by respective bullet bounding boxes and the hand by a bounding box. While these bounding boxes provide locational information, they do not provide information on the orientation of the represented objects, nor in the case of overlapping bounding boxes do they provide information on which object is occluding and which is occluded. 
     On the other hand, in processing of the same image of a magazine loading task using the object detection by instance segmentation  122  of the activity assistance system embodiment of  FIG. 18 , the instance segmentation  122  identifies the magazine by a pixel boundary and likewise identifies each of the bullets by corresponding bullet pixel boundaries and the hand by a pixel boundary. Unlike the bounding boxes produced by the object detection  22  of  FIG. 1 , the pixel boundaries produced by the instance segmentation  122  of  FIG. 18  identify the exact pixel boundaries of the corresponding objects. These pixel boundaries therefore contain information sufficient to identify the orientations of the respective objects. Moreover, where two objects overlap, the order of overlap (that is, which object is the occluding object and which object is the occluded object) can be identified for objects with standard shapes based on which object has its shape “reduced” by occlusion. Hence, in the activity assistance system of  FIG. 18 , the object overlap detection function  32  of  FIG. 1  can be suitably replaced (or augmented) by an object overlap and occlusion detection function  132  in the system of  FIG. 18 , which identifies which both the overlap and which object is occluded. Likewise, and object orientation detection function  136  can be provided to detect the orientation of an object with a standard shape based on the exact pixel boundary identified by the object detection by instance segmentation  122  of the activity assistance system embodiment of  FIG. 18 . Other event detection functions (not shown) can be similarly enabled by identification of the exact pixel boundary of an object, such as identifying the object size (for objects which may vary in size, e.g. quantifying the amount of peanut butter that is spread onto a bread slice in the main illustrative example presented herein). 
     As already noted, the object detection by instance segmentation  122  of the activity assistance system embodiment of  FIG. 18  can facilitate more accurate scripting of activities such as the AMMP magazine loading task. Another possible application thusly enabled is a pill sorting application, in which a person is tasked with sorting pills into a pill organizer. Here the ability to identify the exact pixel boundary of each pill facilitates distinguishing different types of pills, since for example a blood pressure medication pill may have a different shape and/or size compared with another type of pill. (Pill color may also be useful in making such distinctions). The pill sorting task takes advantage of the common pharmaceutical industry practice of employing standard pill sizes, shapes, and colors for different pharmaceutical pills. 
     Another type of task that an benefit from the precise pixel boundary delineation of objects provided by the object detection by instance segmentation  122  of the activity assistance system embodiment of  FIG. 18  is tasks related to diagnosis and/or assessment of visual neglect, which is a neuropsychological condition in which damage to the visual cortex or other brain area relating to vision results in the person having difficulty in recognizing a spatial portion of an observed object. For example, in hemispatial neglect, the damage is to one hemisphere of the brain and typically manifests as reduced or non-existent recognition of one-half of an observed object. In one suitable approach, the person performing the test may be asked to trace the outline of an observed object using a finger, pointing stick, or the like. As the object detection by instance segmentation  122  provides the exact pixel boundary of the object, any systematic difference between this pixel boundary and the outline traced by the person can be identified as potentially due to visual neglect; and, indeed, the spatial portion of the object that the person has difficulty visually perceiving can be similarly identified. The preferred embodiments have been illustrated and described. Obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.