When IoT becomes IoB, the Internet of Behaviors

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The interest in IoB received a boost, when Gartner listed it as a major future technological trend which will touch the amazing amount of 40% of the world population by 2023 – in two years. A decade ahead, 6G and the exploding amount of human behavior data in the net will put more pressure on this development with its accessibility, speed, data transfer capacity, and real-time potential. For r&d, services and businesses this is a call for a timely analysis of what real potential IoB can and will offer.

I’ve been happy to read fresh news and columns taking IoB seriously and looking at the realistic potential, e.g. on assistive technology and IoB for empowering the disabled and older people, International Banker and many others. The ethics problems have been frequently touched, sometimes forgetting that the original idea in IoB was to separate id and behavior data, when it is required – and still offer massive, even global service potential.

Here I give a very short and limited explanation to some aspects of IoB, with the purpose to show why I believe IoB will be a significant aspect of the intelligent digital future and why we need a coding and notation system for classifying IoB behaviors. IoT and IoB will need each other in dealing with behavior data.

From IoT to behavior

Imagine that you have a motion sensor attached to a person’s wrist and you can address it as an IoT device, with an IP address or some other suitable code. Connecting to the device returns the angle or other useful data of the wrist – you receive data on ‘wrist behavior’.  This is only a primitive thought experiment example of a situation where IoB emerges from IoT.

IoT alone can be in trouble when the person being monitored by the ‘wrist sensor device’ takes various roles and the meaning of the wrist data varies accordingly: at work she can be a doctor, treating patients and engaged with other health care activities. Then in the afternoon she commutes, and in the evening, joins a ballet class. Between these activities a number of tasks and situations occur and the sensor reveals her wrist motions and whatever it is capable of sensing. To fully understand the meaning of the wrist data, it is necessary to know what is the exact role of the person at that moment and in that specific situation, that is, what kind of purposeful behavior is ongoing when data is being recorded. This is where IoB emerges from IoT.

The core idea of IoB is to know exactly the situation, the role and behavior occurring at a specific time. When this is known, the data provided by the IoT device (in this primitive example) can be interpreted and a meaning assigned to it. An engineering comment to this could be that there can and will be much more data available, for example about the person’s location, daily schedules, behavior history, and even audio and images that tell exactly what is the situation, the role and the ongoing task.

However, even if this supporting data were available, the recorded behavior data (in this case only the wrist) must be classified so that it is possible to know the aims and meanings of the specific behavior and relate it t other behaviors. Without a systematic coding of behaviors multiple data flows become problematic to interpret. The arm of the dancer and the doctor is the same but the purpose of the wrist movement changes from one behavior and situation to another. Knowing the situation and purpose makes it possible to analyze, give relevant and timely feed-back and provide offers or interventions that are 100% relevant. Recorded streams can then include these behavior codes.

Then there are the subjective intentions of acts. They hide from any straightforward IoT recording and monitoring systems, but they can be valuable and rich material fed by the subject to the IoB, and allow coding of the essence of the intentions. A simple and convenient arrangement is required for this, like voice commands, symbols, gestures and many other UIs can be used. New easy to use solutions will appear when the market for IoB is mature and the needs are known. An interesting relative to IoB is the UI of Spotify, where the user can express his/her feeling and wish by selecting the music genre, the artist or by using any known list. 

A few words of addressing and behavior codes

There are only a few systematic behavior classification schemes, and they are typically meant for certain contexts, like dance, games and emotional face expressions, to mention a few. See e.g. my thoughts on the computability of behavior. What is relevant for a dancer can be irrelevant for a handicapped person, but the IoB and IoT systems servicing them, can have similar technical architectures, where IoB includes a specific behavior coding scheme and is supported by IoT. Intelligent sensor devices and tools for IoT can work together with IoB to improve behavior recognition and related targeting.

In the 2012 IoB article  and in its follow-up I considered IPv6 with its 2¹²⁸ address space as a potential means to manage and offer enough addresses to code and target any human behaviors and why not (dynamically?) reserve address segments for certain globally relevant service-, business-, education- or entertainment related behavior classes. The addresses within a segment need not be fixed and reserved – a set of dynamically defined addresses could be used for specific contexts – if I understand the possibilities of IPv6 right. Well, I have not discussed the limitations of this with IPv6 professionals and have something to learn here.

If indeed the IPv6 addresses are used by a service provider and its clients, the arrangement could alleviate the risk of hacking since the specific IoB ‘codes’ within a segment can be declared and known only by the two parties so that only they know what behavior they represent and the coding could be changed on the fly and vary across individuals.

Architectural solutions are needed and there are several alternatives, some of which aim at keeping the id and behavior data separate, while offering possibilities of even global services, and to secure the safety of the people using IoB.. However, like in IoT, IP addressing is not a necessity for IoB either and other behavior coding schemes can be introduced to different contexts as it happens already. There is much to do in this.

An amusing final note

I hope I don’t sound querulent. Trying to get funding for the IoB project in Finland from the leading tech and business fund some years ago, with my colleague we received puzzled looks and the decision: “It is impossible to understand what IoB could mean and used for.” Later I saw similar reactions, expressly reserved, if not hostile. I got frustrated but then one night, had an idea. Why not include IoB in my fictional story and show what it can do? At the time I was writing my novel “Perceptions of the Les Demoiselles d’Avignon” which will hopefully come out by summer 2021 (it’s in English) and I included one compelling application of the IoB in the story. Well, then came Gartner and the IoB game changed, for a while at least, but IoB lives in the story of fiction science, as I call its genre. 

Behavior computation: Internet of Behaviors (IoB) and human AI

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Here I will introduce shortly some interesting technological trends that seem to lead to an increased need for a systematic framework to represent and code any human behaviors, simple as well as complex, mental and bodily, and their components. The need to manage massive behavioral data is different for computer game industry, gene sciences, IoT world, and robotics. In AI this is becoming a reality and it is the focus here. 

My take is to suggest IoB as a candidate approach, not only in these fields but in general, to model and code human behaviors for computational and then service purposes. This will be a vast task if it is launched. My guess is that the technology-evolutionary development will first lead to context- and device-specific modeling of behaviors, but when the data portability becomes an issue it will then reveal the benefits of a common framework for behavior data, whatever the means of generating (in games, robots) or following (genes, IoT, AI) behaviors might be. This development will take time.

Dance behavior codes are here already

Decades ago, I happened to know the then young Finnish dancer Mikko Nissinen who later became a famous ballet star and a choreographer. He introduced to me a Dutch system for coding choreographies and we discussed the problems of reaching the quality and essence of such expressive movements. Dance notations have a long history and there are dozens of different systems, specific for each style and not to be generalized to other styles. The main aim is to document dances and help to preserve them. Indeed, dance and theatre are perhaps the most ambitious, realistic fields for generating and coding any human behaviors and AI could use these for its future learning needs.

The dancing Asimo robots from 2007 and now Boston Dynamic robots, although they look like machines, demonstrate the vivid human-like potential of robots. The secret behind their marvelous performance is that their behavior imitates human dance – as a cultural and expressive phenomenon – in a credible manner. They act as much as any characters in a movie, theatre play or a game animation.

Dance behaviors – scripts – have been programmed and stored in these robot’s control store and even without knowing the details of the software and algorithms, there must be an integrated set of inter-linked, natural-like behaviors and their elements that can be output in a coordinated and adaptive manner. In other words, dance behaviors can be coded. What about millions of other human behaviors and their components? There is no unique solution to this call, yet.

Behaving AI

The behaving AI of the future is expected to master and imitate human behaviors, emotional, intentional and motor alike, and beat us in any rational task. The functioning of AI is totally different from the processes going on in the human mind and the representation of the behavioral elements of the robots are not the same as in the human mind-body system. The artificial neural networks in deep learning and related ML system are indeed, artificial, perhaps biologically inspired ones.  

AI must learn and be taught to behave in a manner which we humans can observe, interpret, accept, relate to and understand the depicted behavior. With an ever more versatile AI, millions of human behavior patterns, features, expressions, and episodes become difficult to manage systematically. Even more difficult is to build a system that covers, for example, a large set of different behaviors having the same style. Think about a robot that behaves nicely here in Finland. If we then take it to another country, with totally different culture from ours, how should the behavior program be altered or adapted in order for the robot to behave well there? Computational, adaptive and mass storage solutions and some others can be imagined.

Dance notations have been genre specific. A good example is the type of dance performed by different individuals. From the outset dance behavior follows a certain discipline, but variations occur, it is the essence of art. The problem is not easier, when considering the (expressed) motivations and cultural drivers of each behavior or a behavior set. A systematic for the representation and coding of behaviors is needed and it should allow computational manipulation.

Human behaviors and IoT

Many IoT devices and systems will be in close proximity to or in direct contact with behaving people at work, home and in free time. For example, following how people behave according to certain protocols in critical environments can be based on IoT devices which then allow seamless recording and monitoring of the behaviors of individuals who are recognized by identity. Other application areas for IoT are energy, health, and child -related behaviors, and of course, wearable sensors and many others. Common to all these is the need to know, what behaviors are associated with the IoT addressed systems in use and how to classify these behaviors. In case of complex and variable behaviors this is not an easy task to manage.

Synthetic emotional face expressions

In animation, emotional face expressions have been an essential part of character behavior. To put it simply, the question is how to link an assumed mental state, an emotion, with a set of relevant face expressions – that is. visible behaviors. To accomplish this, various methods are used, from GAN to emotion theoretical classification and other component-based systematics. I was lucky to serve as the opponent for the doctoral thesis by Meeri Mäkäräinen, Aalto University “Blending and Exaggeration of Animated Facial Expressions of Emotion” and was inspired to look at this work from a general perspective, too: how to represent and manage data on any complex mental states and their related behaviours so that it serves the needs of the situation, be it a drama, a real life communication episode or communication with or guiding a behaving robot. The field is developing fast and serves as a good model for dealing with any mental human behaviors. There is no general model for representing behaviors although specific toolsets and approaches flourish.

Non-player character behavior in games

Computer games generate different behaviors for the non-player characters and in the present game scene, massive behavior data must be managed. The field develops fast. Typically, dynamic scrips are used to make the characters show realistic or any other wanted behavior, which can have very delicate variations. Adaptive, learning and human like principles can be used for generating relevant behaviors which can be of any human and cultural genre and of course imaginary.

Social robot behavior

Social robotics aims at building robots where natural-like behavior becomes possible. This is accomplished e.g. by combining cognitive architecture, adaptive behavior and emotional expressions which can be used in natural-like human-robot interaction, UI and collaboration.  Applications will occur in numerous contexts from hospitals to schools and industrial settings, even entertainment.

Representation and storage of behavior models is then an essential part of a dynamically behaving robot. At the writing of this, it seems that there is no generally accepted model for representing all human behaviors but the need for a system that covers them is evident.  Social robotics evolves and some of the behavioral-emotional models are based on human cognitive theories and biological process. See e.g. Nocentini et al. 2019”A Survey of Behavioral Models for Social Robots).

Genes and behavior

Gene sciences have managed to characterize the occurrence of important gene expressions in human tissues and in different individuals – and various approaches emerge to understand how human behaviors are guided by genetic processes. Currently there are numerous studies where personality, intelligence and various pathologies are correlated with genetic factors. Typically, however, no direct, expressive and systematic behavior model is used in such studies.

In summary, these fields share the need for a general framework for representing and coding all behaviors, which would allow applying the model to the processing of behavior data, mental and physical alike – and transfer of behavior data. Genetics, learning AI, IoT, computer games and robotics are moving to a direction where the need becomes pressing. 

Digital representation of behaviors in AI

AI systems receive learning/teaching/guiding data. When AI performs a task imitating a simple natural human behavior, for example recognizing human faces or other objects, each input pattern vector represents an object or components of it, which we humans can recognize, too even when they have only a mathematical formulation and do not necessarily appear as natural elements of objects. In the case of face images, the relevant vector spaces can be defined so that they cover and differentiate any natural face images. Teaching and supervising an AI system can then use these representations. The approach works well in any sensory domain where the task is simple, to recognize objects – or at least to classify them.

What about more complex or abstract human behaviors, like solving a mathematical problem, playing basketball, dancing or buying a new home? Playing an instrument, praying, writing a book, dreaming of a new job, composing a piece, thinking of life together with a loved one,  … the list of human behaviors is endless and will continue growing. Clearly, it is a formidable, almost impossible task to list and code all human behaviors and their components. We know how actors manage to move us with their behaviors (which do not originate from real life). Some robots are aiming at evoking strong emotional responses from us, especially in children’s play, therapy, elderly care and sex. More is to come.

We will see an exponential growth in the emergence of digital services and applications which record and want to know and serve relevant human behaviors and situations. Typical examples are physiological trackers for various purposes, map-based-, educational-, health care-, music services and many others. They do not have a common data model for representing behavior and their ways of classifying behaviors vary according to the use context. The coded behaviors hide in silos. Only few of them offer means for representing human mental states/behaviors. There is a babel of behavior data representations. Data Transfer Project run by Apple, Facebook, Google and other giants aims at making data portable from one environment to another. No doubt, one of their challenges is the general management of high-quality behavior data.

Behavior Computation for future AI systems

An efficient framework for behavior representations includes a system for coding different behaviors and behavior classes, which then can be used as a well-defined input to AI as teaching material. It can also be used as a means for ‘humanification’ of AI-system User Interfaces (UI) and for robots to generate certain human-like, IoB coded,  behaviors and especially their situational variations. When the IoB system has matured enough it can be used for any AI applications having human-like behaviors.

While this is not happening, it is a real possibility that AI can learn to browse our behaviors, using any sources from movies to literature, to health and any other history data and to use this knowledge for imitating human behavior and guiding people. AI can generate hypotheses about us in order to predict our behavior and reactions to targeted messaging, and interventions, for example. Already now AI can generate reasonably rational texts and stories, analyze and summarize genuine scientific articles, and even suggest new hypothesis and solutions to complex problems. Deep fake GAN imaging and audio imitate human expressions and styles. To the best of my knowledge, systematics of behavior modeling is only emerging but they do not exist so far.

When AI is made to learn high-level human behaviors, reactions and experiences, which are typical in visual arts and music, creative sports, written culture and simple imagination, it needs systematic guidance information – behavior data – that directs its development.  How far, in the human and cultural realms can AI reach and what can we do to build the best possible human AI?

I have here emphasized the need for behavior model systematics in supporting the digital use of behavior data. This will be important not only to generate various behaviors but to build a genuine behavior computation framework that allows coding, analysis, computation, transformation and learning of any human behavior. Such a system does not exist, yet. More about it later.

Internet of Behaviors (IoB) in good company – future behavior markets

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“Too much power and too much personal data.”
(Tim Berners-Lee in NYT interview, Jan 10 2021).

Having followed Tim Berners-Lee’s comments and activities I happened to read the NYT interview, January 10, 2021 which inspired me to look at the preliminary IoB concept by considering other developments with similar interests in the digital world. Clearly by separating behavior data and identity is a specific property of the IoB framework and it does not occur in other related concepts. However, the common aim and pressure is to protect people and their personal data while at the same time maintain and even boost digital service markets. My vision is that massive, global behavior markets can be opened but we must try to avoid the risks.

Berners-Lee vision is to build infrastructure, systems and services that offer people a secure and useful control of their personal data and transactions.  He has introduced the Personal Online Data Store (POD) – concept where the user controls her own data and can allow selected operators like companies to access and use the data when offering their specific services. Pilot projects have been ongoing.

MyData program has similar aims in trying to return control and data ownerships to us, the users and a chance to benefit from the use and value of our personal data. 

Portable data.  There is a major program Data Transfer Project (DTP), run by Google, Facebook, Apple, MS and Twitter, where the aim is to make personal data, not only behavioral data, portable. There are evident drivers for this collaboration among the data giants, considering the vast amount of personal and behavior data generated in the net. It is clear, however, that portable personal and identity data include a number of well-known risks and some new ones, for sure.

Gartner and IoB

In 2019 Gartner listed Internet of Behavior (IoB) as one of its top 20 strategic technology trends, and in 10/2020 it was already mentioned as Trend 1. Interestingly, originally they used IoB without the ‘s’ which I had included in the 2012a, 2012b  articles. Knowing the huge scope of behaviors and wearing the psychologist’s hat, it was a natural choice for me to include the s, which also hints to the idea that mental phenomena can and must be considered as behaviors in the IoB concept.

Gartner suggests compelling drivers for the emergence of IoB, a coherent means to: Capture behaviors, Analyze, Understand, and Monetize. They do not specifically worry about the protection of personal data but whatever the model for providing security, it should support if not maintain the best of the digital markets.

In Gartner’s vision, pattern recognition, sensor systems and other monitoring technologies are used to recognize ongoing behaviors.  Identity has no special role there and it can be linked with the behavior for various purposes like measuring, follow-up, message targeting and intervention, for example. Naturally, id data is often necessary and extra care is taken to protect it. 

Looking at these technological developments, there looms a major competition between various concepts and architectures, each trying to gain access to service markets or maintain their power in using personal data for businesses and other benefits. A specific but extensive challenge will be how to make personal data safe and secure and to guarantee that it is used for the best of the citizens, customers and any other users. In summary, four new solution types can be seen to emerge:

1. Preparing standards for personal data to make it portable between any operators and platforms and for any uses (DTP)
2. Giving people data ownership and self-determination in managing their data (MyData)
3. Securing personal data and offering secure control of personal traffic, data and transactions with specific permission access rights to service providers (Berners’Lee).
4. Separating behavior data and identity data so that behaviors can be targeted and served without risking personal identity (IoB)

IoB is different from all other three since it includes the original, perhaps transformational idea that identity data and behavior data can be separated when needed and it can be very useful – without disturbing or hindering valuable digital services and markets. The other three approaches are not exclusive and even at the moment there are various versions or feature combinations of these on the market or being developed. At the moment it is difficult to see how this aspect of digital evolution will proceed.

The relevance of human-centric IoB 

When outlining the first concept version of the Internet of Behaviors 2012a, 2012b it was partly based on the conviction that people are not willing or able to invest any extra human effort to manage or own their personal or any other data.  Psychologically, data is secondary. Data protection tools for example, do have benefits like anonymity and security, but in general, they are used because it is a must and a social routine in the risky digital world. 

Managing masses of personal data is a complex task and only if it happens without any burden, almost invisibly, it can be a choice people are motivated to make.  Secondly, thinking about the IoB concept in 2010-11, the bad quality of the marketing/targeting tools was evident in how they use ML and other customer or lead management models to approach people with push offers. When these tools fail, it is typically in relevance and accurate timing. There are very human reasons to this failure, which I have explained in my IoB texts. Indeed, relevance is a most underestimated driver of human behavior and it is typically considered as the opposite to disturbance. Curious enough, it lacks a common behavior-theoretical name even though we all suffer from the irrelevance in the net.

The strongest sources of human interest can be found in relevance, timing, contents, contexts, relationships, experiences, style and sense of presence, to name only a few major factors. I have described this thinking in my book On the Edge of Human behaviors  where I use the ‘Golden Triangle’ framework: Content  orientation – Human centered and care – Psychological ground,  to span the conceptual view.

 Towards IoB architecture

We can construct a general model for representing any behavior, physical or mental,  

in digital form but we have to deal with two major challenges and requirements. Firstly, there is no general coding system for all human behaviors, and secondly, it is a major data-architectural endeavor to build a platform for managing any possible human behavior, physical or mental or even spiritual in nature. Some may think there is no way to code all behaviors, but it is possible to launch an evolutionary process, with no limits and we can expect major potential in future behavior markets.

Both of the requirements can be met by using a version of the IoB behavior data model which grows and improves as experiences are accumulated and the coding is tuned on the way. Services and tools can then be offered to people, based on their expressed or recognized (coded, often ongoing) behaviors, without knowing who they are and where. The architecture for IoB can include e.g. the following modules:

1. Recording modules. A Behavior codec, where the recording and coding of a specific behavior is accomplished A) automatically e.g. by a pattern recognition or AI system , and/or B) by indicators or voluntary expressions of behaviors which are generated by the behaving individuals who also disclose their identities and C) the same as B but where only the ongoing/planned behavior is indicated without knowing who behaves or where the behavior occurs. This latter form was the original idea in my IoB texts. The obtained behavior data is then stored in IoB data base and without id data when needed. The coding of behaviors is contextual.
2. IoB data modules. Behavior decoder provides behavior data (with or without id data) to service providers who can then select and use what is relevant for them. Their interest is to see what direct or potential service needs exist and when, based on the occurring behaviors represented in the IoB data base.
3. IoB service platform. This is where ‘behaving individuals’, that is, citizens, customers, firms – the oB behavior data sources – and why not robots as well, can access any services they want from IoB and which are offered by service providers. Access to the IoB service platform takes place either anonymously or with id data included. Technically, there can be a number of different modules in the platform and they can use any media to inform the behaving individuals about the available services. However, the individuals decide whether to use the offered services.

User perspective. How to arrange for individuals to know what services are available for them and to access them effectively, when they do not want to reveal their identities? In Berners-Lee’s and MyData approaches this is accomplished by specific, secure data management solutions and there are, of course, numerous methods for anonymous communication. However, one possibility, among some others, which I have considered with IoB is to use a message board analogy, where service providers have write-access to personal boards to which only users have read access. This is only one of the many alternatives, and includes suitable options by which users can express their general or specific data to ease targeting.

Market perspective. One might wonder what use it is to know a behavior occurring right now, for example. However, the potential behavior pool can be globally huge so that relevant and timely offers can reach a significant audience –ongoing behavior is always a symbol of relevant needs. Further, through an IoB hub, certain behavior can be served by several providers each of which are not interested in the person but in the need based on the specific behavior. To quote B-L in NYT: “This is about making markets’. I’d could call it future behavior markets. The market potential of IoB is huge even when id data is no used.

Identity protection is available in IoB when needed. The structure and details of this preliminary architecture are open and various alternatives and versions can be imagined and designed. I will return to these later.

In Summary: IoB is one candidate concept for a service framework where customers and citizens can be served, based on their known behavior and do it safely by protecting their identities when needed. This is a call to regulators as well to consider means for identity-safe digital services by separating identity and behavior. Of course, not all behaviors can be served without knowing the identity of the person like in many health care contexts and in specific work environments, for example. But even there it is not always necessary. There is huge market potential in services that do not need identity data, but which serve behaviors accurately, time it right and are relevant to the expressed or recorded behavior.

Evidence based decision making – when going against the norm

Sarah Vallance: A Memoir of My Brain (book). Photo Creative Commons

I write this blog in Finnish, and hope that the Google translator recognizes the essence of the story from my youth – I was 25 then – when I every now and then worked as a clinical neuropsychologist at the University hospital in Helsinki, Finland.  The work with all kinds of neurological patients and their family members had a permanent impact on my thinking about what is a ‘brain’ and what it means when someone suffers from a brain damage or a disorder or of a carotid stroke and mild paralysis, for example, from which I have survived and recovered.

I’ve sometimes mentioned that if St Peter asks me at the famous Gates, “What good have you done in your life?” I have two stories to tell. This is the first one and the second one  – when I was 18 – is about an incidence when I saved an old man (to my eyes then) from under a train leaving from Helsinki Central railways station. Nobody thanked me for it but because the train was made to stop when I was running alongside the moving train and lifted the fellow from the rails, between the wagons, someone, in addition to St Peter must have seen this deed.

I was inspired to write this, having followed the controversy originating from a book and health therapy guidance by a well-known Finnish figure. I don’t have anything to say about the case but reading about it, I remembered these.

Kun on toimittava omaperäisesti

Olen sivusta seurannut Maria Nordinin kirjan poleemista käsittelyä enkä aio siihen ottaa mitään kantaa. Asian ja esitettyjen argumenttien pohdinta palautti elävästi mieleeni erään elämääni ja ajatteluani vahvasti liikuttaneen teon ja kokemukset neuropsykologi-ajoiltani, nuorena 25 v miehenä. Jos Pyhä Pietari joskus kysyy, olenko tehnyt elämässäsi jotakin hyvää, niin tämä on toinen tarinoista, jonka kertoisin. Tässä on hieman pitkä johdanto hyvään tarinaan. Se toinen mielestäni hyvä teko oli tapahtumasarja, jossa pelastin erään vanhemman miehen (olin silloin 18 v) hengen Helsingin Rautatieasemalla, kun hän oli pudonnut liikkuvan junan vanujen väliin.

Afasiakuntoutus

Olin vuonna 1972 keväällä lupautunut afasiapotilaiden kesäisen kuntoutuskurssin vetäjäksi. Mukaan lähtisi n 25 potilasta pääosin ilman omaisia. Mukaan tuli muitakin terapeutteja, aivan upeita ihmisiä: Sari Kaukonen puheterapeuttina ja Raija Ahvonen sosiaalityöntekijänä, mutta ei lääkäriä. Jos oikein muistan niin Leena Warsell oli silloin oivana ja hyvähenkisenä fysioterapeuttina mukana, kuten myös pari afasiayhdistyksen ihmistä.

Tällaista afasiakurssia ei oltu aiemmin järjestetty ja piti Kelan ihmisten kanssa jopa keksiä sille sopiva nimike, jolle hyvä rahoitusmomentti löytyisi.  Syntyi ”sopeutumisvalmennuskurssi”. Käsittääkseni se oli ensimmäinen nimikkeeltään ja senkin jälkeen käytössä.

Saimme leiritilat Kiteen Kristilliseltä Kansanopistolta, jonne kuumana kesäpäivänä matkustettiin koko joukko samaan aikaan junalla. Ajatuksenani oli, että järjestämme leirillä sekä neuropsykologisen diagnostiikan että yksilökohtaisen kuntoutusharjoituksen, josta potilaat sitten voisivat omassa kuntoutumisessaan hyötyä. Tämä oli hyvin rationaalista sillä tuohon aikaan oli hyvin tiedossa, että monet lääkärit ja muutkin epäilivät kuntoutuksen tehoa, kun näyttöä ei juuri ollut ja neuropsykologisen kuntoutuksen ja diagnostiikan tutkimukset olivat sekalaisia ja suorastaan huonoja.  Ajateltiin, että vaurioituneet aivot eivät olennaisesti toivu noin vuoden-parin jälkeen vammasta. Olin kuitenkin varma, että mm. Lurian oppien soveltaminen olisi avuksi ja olinkin niitä omin päin opiskellut ja kokeillut klinikassa sekä käynyt myös erään innostavan kurssin aihepiiristä. Olin lukenut kaikki Lurian käännetyt teokset ja paljon muuta.

Ei siis huolta, koko jengi junaan ilman lääkäriä. Mukana oli hemipleegikkoja, jotka liikkuivat huonosti, jos lainkaan, muita halvauksia, monilla oli epileptisiä oireita eikä juuri kukaan kyennyt puhumaan normaalisti tai puhui, mutta sokeltaen, hitaasti, pulputen kontrolloimattomasti –  tai ei sanonut sanaakaan.

Perillä odotti upea kesäpaikka ja saimme testi- ja terapiarutiinit  sekä yhdessäolon hienosti käyntiin. Heti alkupäivinä potilaakseni tuli Manu Rytisalmi, jo edesmennyt, entinen musiikkiliikkeen pitäjä Kotkasta, Suomen ensimmäinen päätoiminen jazz-kriitikko ja muusikkojen arvostama ammattimies, joka oli menettänyt kaiken tämän elämänpiirinsä aivoverenvuodon seurauksena. Se oli minulle ’kutsu’ miettiä syvemmin, mitä afasia merkitsee ihmisen elämässä. En ole unohtanut oppia, jonka sain, kun hän änkyttäen ja sokeltaen kertoi, viitaten terapia- ja diagnostiikkaintooni, hyvin hitaasti yksittäisiä sanoja tavoitellen, hymyillen:

”Göte, tajuatko sinä, minkälaista on olla afaatikko? Mietipä tätä. Kuvittele, että kätesi ovat sidottuna selän taakse ja sinun pitäisi selittää, käsiä käyttämättä, mitä tarkoittaa sana ’kuohkea’. Minulla on tämä tunne ja olo joka ikinen hetki.”

Tumma Manu oli ulkomaalaisen tai ehkä romaanin näköinen ja kun hän kävi Alkossa niin hänen luultiin huonon puheen vuoksi olevan humalassa eikä hänelle myyty. Sitten hän keksi pukeutua turkispomppaan talvella, ja esiintyä ”huonoa Suomea” puhuvana diplomaattina. Manulla riitti omaperäisen luovia tapoja selviytyä vaikeasta tilanteestaan ja kuulin muiden potilaiden kiittävän häntä näistä ideoista ja hänen voimaannuttavasta olemuksestaan ja optimismista, jota neurologiset potilaat aina tarvitsevat (tosin silloin ei ollut käytössä sana ’voimaannuttava’). Myöhemmin hän ryhtyi käsikirjoittajaksi sanellen työnsä ja voitti palkintoja mm huumenuorten maailmasta ja Uuno Klamista kertovilla dokumenteillaan.

Leirillä oli mukana myös nuori n 19-vuotias poika, joka oli moottoripyöräonnettomuuden seurauksena täysin puhekyvytön ja muitakin oireita hänellä oli. Hän ei kyennyt sanomaan sanaakaan. Pohjoisessa Suomessa ei ollut parempaa hoitopaikkaa tarjolla, joten hän oli siellä vanhainkodissa.

Eräs hyväntuulinen ja eloisa nuori mies puhua puputti kuin ruuneperi, mutta kärsi sekä Wernicken afasiasta että suun apraksiasta: tämän tuloksena hän puhui kyllä, mutta ei tajunnut omaa puhettaan. Alkuun hän herätti hieman ihmetystä oudolla, värikkäästi intonoidulla pulinallaan. Hänen eleensä ja kasvonilmeensä olivat hyvin eläväisiä ja osallistuvia ja kun testasin hänet leirillä eräällä vaativalla visuaalisen päättelyn testillä, niin hän sai siinä harvinaiset lähes täydet pisteet. Sen jälkeen häntä oli helppo kuunnellakin ja tietää, että hänellä oli aina kerrottavaa. Hän oli ammatiltaan sähköteknikko.

Mukana oli itsemurhan yrittäjiä ja sen seurauksena vammautuneita, auto-onnettomuudessa vakavasti loukkaantuneita ja aivotukoksesta tai verenvuodosta kärsineitä. Sairaus oli järisyttänyt kaikkien elämää enemmän kuin vammoista ja vaivoista olisi aina voinut suoraan arvioida.

Päivät olivat meille työntekijöille pitkiä, mutta piankin tutustuimme leiriläisiin ja kuulin toisen  ’kutsuni’, kun muutaman päivän päästä leirillä huomasin, että en enää ’nähnyt,’ että joku oli halvaantunut, enkä enää kuullut heidän puhettaan huonona. Aloin nähdä ja kuulla ihmisen, johon on yhteys. Usein näin käy kotipiirissä, kun perheenjäsen vammautuu. Ulkopuoliset vierastavat vammautunutta useinkin, varsinkin jos tämä on hyvä tuttu.

Erään pitkän päivän päätteeksi istuimme Sari, minä ja Raija eräässä kylän kahviossa rentoutumassa ja pohtimassa tekemisiämme. Muistan tuon hetken unohtumattomalla lämmöllä. Kävimme läpi kohtaamiemme potilaiden kohtaloita ja oli selvää, että kysymys ei ole vain potilaista, vaan näiden ihmisten elämästä. Toistuvasti nousi esiin, mitä muuta voisimme tehdä, kuin tämän hyvin suunnitellun kuntoutusohjelman ja leirielämän.

Syntyi yhteistuumin villi ajatus: 

Viedään koko jengi Kiteen Kantakrouviin hyvälle illalliselle! Vammojen vuoksi potilaillamme ei ollut näitä kokemuksia lähiajoilta juuri lainkaan. Krouvi oli ehkä hieman alle kilometrin päässä leiripaikastamme, joten sinne pitäisi kävellä – ja tietenkin tulla takaisin. Muistaakseni meillä oli yksi polkupyörä.

Varasimme pöydät, laadimme matkasuunnitelman eli kuka auttaa ketäkin ja miten, jotta kykenisimme kävelemään krouviin. Puoliksi halvaantuneiden liikkuminen oli vaikeaa eikä ollut itsestään selvää kenestä olisi apua ja tukea kenellekin. Saimme sitten aikaan yhteistyöstrategian, jolla kaikki pääsimme sitten eräänä iltana matkaan, kohti Kantakrouvia ja jokainen sai tarvitsemansa tuen. Muutamalla täydellisestä afasiasta kärsineellä miehellä ei ollut liikkumisvaikeuksia ja heistä oli paljon apua. Ryhmä ei todellakaan ollut tyypillinen näky Kiteen kauniin kesäisellä raitilla, eikä varsinkaan ravintolassa.

Pöytään asettuminen sujui samalla luovalla strategialla, mutta sitten tuli tilausten aika. Juuri kukaan leiriläisistä ei itse kyennyt tilamaan aluksi mitään eli hoidimme tilaukset jokaisen puolesta ruokineen ja juomineen. 

Juomia eli viinejä, olutta ja alkudrinkkejä tilattaessa ystävällinen, mutta varautuneen oloinen naistarjoilija jututti vakavan näköisenä minua, Saria ja Raijaa mutta olimme jollakin tavoin riittävän vakuuttavia (nuoria), että saatiin pöytään mitä haluttiin, vaikka neuvottelut olivat tässäkin varsin omaperäinen koreografia. Kuinka tilata VodkaSprite ilman sanoja tai kykyä lukea?. Kunpa muistaisin tämän hienon tarjoilijarouvan, että osaisin vieläkin kiittää tästä luottamuksesta ja suhtautumisesta meihin! Riski saattoi näyttää melkoiselta ja epäilemättä tarjoilijallamme oli mielessään Alkon tarkastustoiminta.

Lihan leikkaamiseenkin tarvittiin siellä täällä apua, mutta leiriläiset kykenivät ongelmistaan huolimatta jo hienosti auttamaan toinen toisiaan ja pyytämään apua kaverilta. Joku käsi toimi aina, vaikka puhetta vain harva hallitsi riittävästi ja siinä tarvittiin meitä terapeutteja.

Ilta eteni värikkäästi ja ilmassa oli ainutlaatuista iloa, se oli helppo nähdä ja kokea mukana. Kuten voi arvella, tässä joukossa ei virinnyt aivan tavallista ravintolan puheensorinaa, todellakaan, mutta ääntä riitti ja oli hauskaa. Ymmärrettiin toinen toisiamme, monin eri tavoin. Krouvin naapuripöydissä ehkä ihmeteltiin, minkä ihmeen maan kielistä oli kysymys, mutta kukaan ei kommentoinut meitä ikävästi, ei edes elein. Joku saattoi käydä ystävällisesti kysymässä, mistä tulemme.

Usein ilta ravintolassa, varsinkin loppuilta, käy automaattisesti. ’Potilamme’ jotka olivat saanet ensimmäiset drinkkinsä osasivat tilata toimivaa kättä heilauttaen, sanaakaan sanomatta, että ”yksi samanlainen vielä” ja ilta eteni sujuvasti. 

En enää muista missä vaiheessa alkoi tuntua siltä, että oli hyvä aika koota joukko ja järjestäytyä keskinäiseen takaisin kuljettautumiseen. Sama malli toimi kuin tullessakin, mutta nyt oli huoli ja pelko tiessään, kun tiesimme että kaikki onnistuu ja oli syntynyt uusia ystävyyksiä. Tiedettiin, kuka tarvitsee mitäkin apua ja osattiin pyytää sitä. Olihan se oudolla tavalla sekalainen seurakunta eikä aivan mutkatta kulkeva, mutta puheen tapaista juttua riitti, kun noustiin kesäillan hämärässä kohti Kristillistä kansanopistoa.

Saimme melkein koko joukon (jatkoja tuntui riittävän) levolle joskus keskiyön aikaan, mutta jäimme vielä Sari, Raija ja minä pohtimaan mitä oli tapahtunut. Se ei ikinä unohdu. Harvoin on niin puhtaan hyvä olo jostakin yhdestä elämän episodista eikä edes osannut olla helpottunut. Olisihan tässä joukossa voinut tapahtua vaikka mitä eikä mukana ollut lääkäriä.

Seuraavan päivän aamiaisella opiston rehtori piti meille kevyen saarnan. Kylällä oli kuulemma nähty outo joukko Kristillisen opiston vieraita Krouvissa ja pitämässä ääntä raitilla. Tämä ei nyt saisi enää toistua. Luulen kuitenkin, että hän oli jonkun verran armollinen meille, sillä mitään isompaa juttua tästä ei syntynyt, sain vain kevyen puhuttelun,   – eikä edes sen jälkeen, kun teimme vielä uudenkin, tällä kertaa lähtiäisvierailun samalla strategiamallilla. Nyt jo ravintolan kantapeikotkin ottivat meidät hymyssä suin vastaan.

Evidence-based terapia? Ihailen edelleenkin tuon tiimimme humaania ja rohkeata otetta ja kykyä ajatella ja toimia jollakin ’muulla tavoin’, joka voisi olla hyväksi. Mitään tällaista kokemusta ei meillä aiemmalta lyhyeltä uraltamme ollut. Itselläni ehkä tausta ravintoloitsijan poikana oli saanut aikaan sen, että ravintola voi olla hengeltään myös kuin koti tai ainakin jotakin muuta kuin synnin ja lankeemuksen pesä.

Tuskinpa tarjolla on nytkään evidence-artikkelien tarjontaa, jossa on tutkittu tällaisen ”intervention” efektiä eli mitä ne vaikutukset ovat ja kuinka tällaiseen ratkaisuun ylipäätänsä voi kukaan terapeutti rationaalisesti päätyä. En tosin ihmettelisi, jos vaikka Alkon tarkastajien tai poliisikertomuksissa niistä riittäisi juttuja enemmänkin, mutta tuskin Kiteeltä. Tottakai nyt arvaan ja tiedänkin, että uudet kokonaisvaltaiset terapiat katsovat ihmistä laajemmin eli potilas on ihminen, siitä ei ole epäilystä, mutta matka ihmisten krouviin voi niissäkin olla vielä pitkä. Liikutaan vallitsevien disipliinien ulkorajoilla.